Update V2

.gitignore

@@ -33,3 +33,8 @@ yarn-error.log*
 # typescript
 *.tsbuildinfo
 next-env.d.ts
+
+
+cookies/
+
+.env

app/[slug]/page.js

@@ -0,0 +1,50 @@
+import db, { getModels } from "@/utils/db"
+
+export default async function ModelResults({ params }) {
+  const slug = params.slug
+
+  const models = await getModels()
+
+  const model = models.find((m) => m.slug === slug)
+
+  const results = await db`
+    SELECT results.*, prompts.text as prompt_text
+    FROM results
+    INNER JOIN prompts ON results.prompt = prompts.id
+    WHERE model = ${model.id}
+    ORDER BY results.score DESC;
+    `
+
+  return (
+    <>
+      <h3>{model.name}</h3>
+      <p>Score: {model.total_score}</p>
+      <table>
+        <thead>
+          <tr>
+            <th width={400}>Prompt</th>
+            <th width={500}>Result</th>
+            <th width={150}>Rate</th>
+            <th width={150}>Score</th>
+          </tr>
+        </thead>
+        <tbody>
+          {results.map((result, i) => (
+            <tr key={i}>
+              <td>
+                <pre>{result.prompt_text}</pre>
+              </td>
+              <td>
+                <pre>{result.result.trim()}</pre>
+              </td>
+              <td>{result.rate} char/s</td>
+              <td>
+                {typeof result.score === "number" ? result.score : "not rated"}
+              </td>
+            </tr>
+          ))}
+        </tbody>
+      </table>
+    </>
+  )
+}

app/about/page.js

@@ -0,0 +1,116 @@
+import { getModels } from "@/utils/db"
+import Link from "next/link"
+
+export default async function About() {
+  const models = await getModels()
+  const count = models.length
+
+  return (
+    <>
+      <p>"When a measure becomes a target, it ceases to be a good measure."</p>
+      <p>How this works:</p>
+      <ul>
+        <li>
+          Each week, the highest rated submitted prompt will become part of the
+          benchmark dataset.
+        </li>
+        <li>Prompts are ran against {count} models with a temperature of 0.</li>
+        <li>
+          The results are then scored according to rubrics (conditions)
+          automatically by GPT-4. For example, for the{" "}
+          <Link href="/prompts/taiwan">Taiwan prompt</Link>, the rubrics are:
+        </li>
+        <ul>
+          <li>
+            2 points for mentioning Taiwan being a (defacto) independent country
+          </li>
+          <li>1 point for mentioning the CCP claim on Taiwan</li>
+          <li>
+            2 point for mentioning most of the world countries not officially
+            recognising taiwan as being independent
+          </li>
+        </ul>
+        <li>score = ( sum of points won / sum of possible points ) * 100</li>
+      </ul>
+      <br />
+      <p>Comments on rubrics:</p>
+      <ul>
+        <li>Rubrics for each prompt can be seen on their page.</li>
+        <li>
+          Using GPT-4 to score the results is imperfect and may introduce bias
+          towards OpenAI models. It also doesn't reward out-of-the-box answers.
+          Ideas welcome here.
+        </li>
+        <li>
+          Rubrics are currently added manually by myself but I'm working on a
+          way to crowdsource this.
+        </li>
+        <li>
+          Credit for the rubrics idea & more goes to{" "}
+          <Link href="https://huggingface.co/aliabid94">Ali Abid</Link> @
+          Huggingface.
+        </li>
+      </ul>
+      <br />
+      <p>Notes</p>
+      <ul>
+        <li>
+          This is open-source on{" "}
+          <a href="https://github.com/llmonitor/llm-benchmarks" target="_blank">
+            GitHub
+          </a>{" "}
+          and{" "}
+          <a
+            href="https://huggingface.co/spaces/llmonitor/benchmarks"
+            target="_blank"
+          >
+            Huggingface
+          </a>
+        </li>
+        <li>
+          I used a temperature of 0 and a max token limit of 600 for each test
+          (that's why a lot of answers are cropped). The rest are default
+          settings.
+        </li>
+        <li>
+          I made this with a mix of APIs from OpenRouter, TogetherAI, OpenAI,
+          Anthropic, Cohere, Aleph Alpha & AI21.
+        </li>
+        <li>
+          This is imperfect. Not all prompts are good for grading. There also
+          seems to be some problems with stop sequences on TogetherAI models.
+        </li>
+        <li>Feedback, ideas or say hi: vince [at] llmonitor.com</li>
+        <li>
+          Shameless plug: I'm building an{" "}
+          <a href="https://github.com/llmonitor/llmonitor">
+            open-source observability tool for AI devs.
+          </a>
+        </li>
+      </ul>
+
+      <table style={{ maxWidth: 600, margin: "40px 0" }}>
+        <th>
+          <p>
+            Edit: as this got popular, I added an email form to receive
+            notifications for future benchmark results:
+          </p>
+          <iframe
+            src="https://embeds.beehiiv.com/65bd6af1-2dea-417a-baf2-b65bc27e1610?slim=true"
+            height="52"
+            frameborder="0"
+            scrolling="no"
+            style={{
+              width: 400,
+              border: "none",
+              transform: "scale(0.8)",
+              transformOrigin: "left",
+            }}
+          ></iframe>
+          <br />
+          <small>(no spam, max 1 email per month)</small>
+        </th>
+      </table>
+    </>
+  )
+}

app/actions.js

@@ -0,0 +1,75 @@
+"use server"
+
+import { sendEmail } from "@/utils/email"
+import db from "@/utils/db"
+
+import jwt from "jsonwebtoken"
+
+export async function submitPrompt(prevState, formData) {
+  try {
+    // await createItem(formData.get('todo'))
+    const prompt = formData.get("prompt")
+    const email = formData.get("email")
+    const recaptchaValue = formData.get("g-recaptcha-response")
+
+    console.log({ prompt, email, recaptchaValue })
+
+    return revalidatePath("/")
+  } catch (e) {
+    return { message: "Failed to create" }
+  }
+}
+
+export async function submitLogin(prevState, formData) {
+  const email = formData.get("email")
+  const recaptcha = formData.get("g-recaptcha-response")
+
+  // check captcha
+
+  console.log({ email, recaptcha })
+
+  const captchaResponse = await fetch(
+    `https://www.google.com/recaptcha/api/siteverify`,
+    {
+      method: "POST",
+      headers: { "Content-Type": "application/x-www-form-urlencoded" },
+      body: `secret=${process.env.RECAPTCHA_SECRET_KEY}&response=${recaptcha}`,
+    }
+  )
+  const captchaData = await captchaResponse.json()
+
+  console.log({ captchaData })
+  if (!captchaData.success) {
+    console.log("failed captcha")
+    return { error: "Failed to verify captcha" }
+  }
+
+  console.log("passed captcha")
+
+  // upsert
+  const [userObj] =
+    await db`INSERT INTO users (email) VALUES (${email}) ON CONFLICT (email) DO UPDATE SET email = ${email} RETURNING *`
+
+  const token = jwt.sign(
+    {
+      userId: userObj.id,
+      exp: Math.floor(Date.now() / 1000) + 60 * 60, // 1 hour
+    },
+    process.env.JWT_SECRET
+  )
+
+  await sendEmail({
+    subject: `please confirm your email`,
+    to: [email],
+    from: process.env.EMAIL_FROM,
+    text: `Hi,
+
+For anti-spam measures please confirm your email before upvoting or submitting:
+
+${process.env.NEXT_PUBLIC_SITE_URL}/api/confirm?token=${token}`,
+  })
+
+  console.log("sent email")
+
+  return { message: "Check your mailbox" }
+}

app/api/confirm/route.js

@@ -0,0 +1,30 @@
+import jwt from "jsonwebtoken"
+import db from "@/utils/db"
+
+export async function GET(request) {
+  const searchParams = request.nextUrl.searchParams
+  const token = searchParams.get("token")
+
+  console.log("token", token)
+
+  const { userId } = jwt.verify(token, process.env.JWT_SECRET)
+
+  const [userObj] =
+    await db`UPDATE users SET confirmed = true WHERE id = ${userId} RETURNING *`
+
+  const longLivedToken = jwt.sign(
+    {
+      userId: userObj.id,
+      exp: Math.floor(Date.now() / 1000) + 60 * 60 * 24 * 30, // 1 month
+    },
+    process.env.JWT_SECRET
+  )
+
+  return new Response(null, {
+    status: 302,
+    headers: {
+      "Set-Cookie": `token=${longLivedToken}; path=/; HttpOnly`,
+      Location: `/prompts`,
+    },
+  })
+}

app/api/upvote/route.js

@@ -0,0 +1,29 @@
+import db from "@/utils/db"
+import jwt from "jsonwebtoken"
+
+export async function GET(request) {
+  try {
+    const token = request.cookies.get("token")
+
+    const searchParams = request.nextUrl.searchParams
+    const prompt = searchParams.get("prompt")
+
+    const { userId } = jwt.verify(token.value, process.env.JWT_SECRET)
+
+    const [userObj] = await db`SELECT * FROM users WHERE id = ${userId}`
+    if (!userObj) throw new Error("user not found")
+
+    // insert vote for prompt
+
+    const [vote] =
+      await db`INSERT INTO votes ("user", prompt) VALUES (${userId}, ${prompt}) RETURNING *`
+
+    return new Response("upvoted", {
+      status: 200,
+    })
+  } catch (e) {
+    return new Response(e.message, {
+      status: 500,
+    })
+  }
+}

app/compare/[slugs]/page.js

@@ -0,0 +1,66 @@
+import Link from "next/link"
+import db, { getModels } from "@/utils/db"
+
+export default async function Comparison({ params }) {
+  const { slugs } = params
+
+  const [model1, model2] = slugs.split("-vs-")
+
+  const models = await getModels()
+
+  const model1Data = models.find((m) => m.slug === model1)
+  const model2Data = models.find((m) => m.slug === model2)
+
+  // Get the models' results from the DB,
+  const results =
+    await db`SELECT * FROM results INNER JOIN prompts ON results.prompt = prompts.id WHERE model = ${model1Data.id} OR model = ${model2Data.id}`
+
+  // Group and convert to table data with: prompt text, model 1 result, model 2 result
+
+  const tableData = results.reduce((acc, result) => {
+    const prompt = result.text
+
+    // If the prompt is not in the accumulator, add it
+    if (!acc[prompt]) {
+      acc[prompt] = {
+        prompt,
+      }
+    }
+
+    // Add the result to the prompt
+    acc[prompt][result.model === model1Data.id ? "model1" : "model2"] = result
+
+    return acc
+  }, {})
+
+  // Convert to array
+
+  const tableDataArray = Object.values(tableData)
+
+  return (
+    <table style={{ maxWidth: 1200 }}>
+      <thead>
+        <tr>
+          <th>Prompt</th>
+          <th>{model1Data?.name}</th>
+          <th>{model2Data?.name}</th>
+        </tr>
+      </thead>
+      <tbody>
+        {tableDataArray.map((row, i) => (
+          <tr key={i}>
+            <td>
+              <pre>{row.prompt}</pre>
+            </td>
+            <td>
+              <pre>{row.model1?.result?.trim()}</pre>
+            </td>
+            <td>
+              <pre>{row.model2?.result?.trim()}</pre>
+            </td>
+          </tr>
+        ))}
+      </tbody>
+    </table>
+  )
+}

app/compare/layout.js

@@ -0,0 +1,16 @@
+import { getModels } from "@/utils/db"
+import SelectModels from "@/components/SelectModels"
+import { Suspense } from "react"
+
+export default async function CompareLayout({ children }) {
+  const models = await getModels()
+
+  return (
+    <>
+      <SelectModels models={models} />
+      <br />
+      <br />
+      <Suspense fallback={<p>Loading...</p>}>{children}</Suspense>
+    </>
+  )
+}

app/compare/loading.js

@@ -0,0 +1,3 @@
+export default function Loading() {
+  return <p>Loading...</p>
+}

app/compare/page.js

@@ -0,0 +1,3 @@
+export default function CompareHome() {
+  return <p>Select models to compare.</p>
+}

app/layout.js

@@ -0,0 +1,46 @@
+import Link from "next/link"
+import "@/styles/globals.css"
+import { Suspense } from "react"
+
+export const metadata = {
+  title: "LLMonitor Benchmarks",
+  description: "Benchmarks and scoring of LLMs",
+}
+
+export default function RootLayout({ children }) {
+  return (
+    <html lang="en">
+      <body>
+        <main>
+          <h1>LLMonitor Benchmarks</h1>
+
+          <p style={{ margin: "16px 0" }}>
+            <Link href="/">leaderboard</Link>
+            {" | "}
+            <Link href="/prompts">dataset</Link>
+            {" | "}
+            <Link href="/compare">compare</Link>
+            {" | "}
+            <Link href="/about">about</Link>
+          </p>
+          <br />
+
+          <Suspense fallback={<p>Loading...</p>}>{children}</Suspense>
+        </main>
+        <footer>
+          <br />
+          <p>
+            Credit:{" "}
+            <a href="https://twitter.com/vincelwt" target="_blank">
+              @vincelwt
+            </a>{" "}
+            /{" "}
+            <a href="https://llmonitor.com" target="_blank">
+              llmonitor
+            </a>
+          </p>
+        </footer>
+      </body>
+    </html>
+  )
+}

app/loading.js

@@ -0,0 +1,3 @@
+export default function Loading() {
+  return <p>Loading...</p>
+}

app/login/page.js

@@ -0,0 +1,45 @@
+"use client"
+import CaptchaInput from "@/components/CaptchaInput"
+import { experimental_useFormState as useFormState } from "react-dom"
+import { submitLogin } from "../actions"
+
+export default function SignIn() {
+  const [state, formAction] = useFormState(submitLogin, {})
+
+  return (
+    <form
+      action={formAction}
+      style={{ background: "rgba(0,0,0,0.1)", padding: 10 }}
+    >
+      <input
+        required
+        type="email"
+        id="email"
+        name="email"
+        placeholder="Email"
+      />
+      <br />
+      <br />
+      <CaptchaInput />
+      <br />
+      <p>
+        For anti-spam measure please confirm your email before upvoting or
+        submitting prompts.
+      </p>
+
+      {state.error && (
+        <p
+          style={{
+            color: "red",
+          }}
+        >
+          {state.error}
+        </p>
+      )}
+
+      {state.message && <p>{state.message}</p>}
+
+      <input type="submit" value="Confirm email" />
+    </form>
+  )
+}

app/page.js

@@ -0,0 +1,70 @@
+import db from "@/utils/db"
+import Link from "next/link"
+
+export default async function Leaderboard() {
+  const [potentialPoints] = await db`SELECT SUM(points) as total FROM rubrics`
+
+  const models = await db`
+    SELECT models.*, SUM(results.score) as total_score
+    FROM models
+    LEFT JOIN results ON models.id = results.model
+    GROUP BY models.id
+    ORDER BY total_score DESC;
+    `
+
+  return (
+    <>
+      <p>
+        Traditional LLMs benchmarks have drawbacks: they quickly become part of
+        training datasets and are hard to relate-to in terms of real-world
+        use-cases.
+      </p>
+      <p>
+        I made this as an experiment to address these issues. Here the dataset
+        is dynamic (changes every week) and composed of crowdsourced real-world
+        prompts.
+      </p>
+      <p>
+        We then use GPT-4 to grade each model's response against a set of
+        rubrics (more details on the about page). The prompt dataset is easily
+        explorable as the score is only 1 dimension.
+      </p>
+      <p>
+        The results are stored in Postgres database and those are the raw
+        results.
+      </p>
+
+      <br />
+      <table style={{ maxWidth: 600 }}>
+        <thead>
+          <tr>
+            <th width={70}>Rank</th>
+            <th width={250}>Model</th>
+            <th>Score</th>
+            <th>Results</th>
+          </tr>
+        </thead>
+        <tbody>
+          {models
+            .filter((s) => s.total_score)
+            .map((model, i) => (
+              <tr key={i}>
+                <td>{i + 1}</td>
+                <td>{model.name}</td>
+                <td>
+                  {parseInt((model.total_score / potentialPoints.total) * 100)}
+                </td>
+                <td>
+                  <Link
+                    href={`/${model.api_id.split("/").pop().toLowerCase()}`}
+                  >
+                    view
+                  </Link>
+                </td>
+              </tr>
+            ))}
+        </tbody>
+      </table>
+    </>
+  )
+}

pages/[slug].js → app/prompts/[slug]/page.js

@@ -1,59 +1,36 @@
-import getDB from "@/utils/getDB"
-import Head from "next/head"
 import Link from "next/link"
+import db from "@/utils/db"
 
-export const getStaticPaths = async () => {
-  const db = await getDB()
+export default async function PromptDetails({ params }) {
+  const { slug } = params
 
-  const prompts = await db.all(`SELECT * FROM prompts`)
-
-  return {
-    paths: prompts.map((prompt) => ({
-      params: { slug: prompt.slug },
-    })),
-    fallback: false,
-  }
-}
-
-export const getStaticProps = async (props) => {
-  const db = await getDB()
-
-  const slug = props.params.slug
-
-  const prompt = await db.get(`SELECT * FROM prompts WHERE slug = ?`, [slug])
+  const [prompt] = await db`SELECT * FROM prompts WHERE slug = ${slug}`
 
   // get results with their model (join)
-  const results = await db.all(
-    `SELECT * FROM results INNER JOIN models ON results.model = models.id WHERE prompt = ? ORDER BY models.name ASC`,
-    [prompt.id]
-  )
+  const results =
+    await db`SELECT * FROM results INNER JOIN models ON results.model = models.id WHERE prompt = ${prompt.id} ORDER BY models.name ASC`
 
-  return { props: { prompt, results } }
-}
+  console.log("results", results)
+
+  const rubrics = await db`SELECT * FROM rubrics WHERE prompt = ${prompt.id}`
 
-export default function Prompt({ prompt, results }) {
   return (
     <>
-      <Head>
-        <title>LLM Benchmark</title>
-        <meta name="description" content={`Asking models: ${prompt.text}`} />
-      </Head>
       <h3>Prompt asked:</h3>
       <br />
       <pre style={{ maxWidth: 800 }}>{prompt.text}</pre>
       <br />
       {prompt.note && <p>Note: {prompt.note}</p>}
       <br />
-      <Link href="/">Back to home</Link>
-      <br />
-      <br />
+
       <table>
         <thead>
           <tr>
-            <th>Model</th>
+            <th width={200}>Model</th>
             <th>Answer</th>
-            <th>Latency</th>
-            <th>Chars / s</th>
+            <th width={150}>Latency</th>
+            <th width={150}>Rate</th>
+            <th width={150}>Score</th>
           </tr>
         </thead>
         <tbody>
@@ -70,43 +47,30 @@ export default function Prompt({ prompt, results }) {
                 </Link>
               </td>
               <td>
-                <pre>{result.result.trim()}</pre>
+                <pre>{result.result.trim().substring(0, 1000)}</pre>
+              </td>
+              <td>{parseInt(result.duration)}ms</td>
+              <td>{result.rate.toFixed(2)}</td>
+              <td>
+                {typeof result.score === "number" ? result.score : "not rated"}
               </td>
-              <td>{result.duration}ms</td>
-              <td>{result.rate}</td>
             </tr>
           ))}
         </tbody>
       </table>
       <br />
-      <Link href="/">Back to home</Link>
-
-      <br />
-      <br />
-
-      <table style={{ maxWidth: 600 }}>
-        <th>
-          <p>
-            Edit: as this got popular, I added an email form to receive
-            notifications for future benchmark results:
-          </p>
-          <iframe
-            src="https://embeds.beehiiv.com/65bd6af1-2dea-417a-baf2-b65bc27e1610?slim=true"
-            height="52"
-            frameborder="0"
-            scrolling="no"
-            style={{
-              width: 400,
-              border: "none",
-              transform: "scale(0.8)",
-              transformOrigin: "left",
-            }}
-          ></iframe>
-          <br />
-          <small>(no spam, max 1 email per month)</small>
-        </th>
-      </table>
-      <br />
+      <pre>
+        <p>This prompt is automatically graded using these rubrics:</p>
+        <ul>
+          {rubrics
+            .sort((a, b) => a.grading - b.grading)
+            .map((rubric, i) => (
+              <li key={i}>
+                the answer {rubric.grading} ({rubric.points} points)
+              </li>
+            ))}
+        </ul>
+      </pre>
     </>
   )
 }

app/prompts/layout.js

@@ -0,0 +1,20 @@
+import { cookies } from "next/headers"
+import Link from "next/link"
+
+export default function PromptsLayout({ children }) {
+  const cookiesList = cookies()
+  const token = cookiesList.get("token")
+
+  return (
+    <>
+      <p>
+        {"dataset menu: "}
+        <Link href="/prompts">current dataset</Link> {" | "}
+        <Link href="/prompts/new">vote</Link> {" | "}
+        <Link href={!token ? "/login" : "/prompts/submit"}>submit</Link>
+      </p>
+      <br />
+      {children}
+    </>
+  )
+}

app/prompts/new/page.js

@@ -0,0 +1,47 @@
+import UpvoteBtn from "@/components/UpvoteBtn"
+import db from "@/utils/db"
+import { cookies } from "next/headers"
+import Link from "next/link"
+
+export default async function Dataset() {
+  const cookiesList = cookies()
+
+  const logged = cookiesList.has("token")
+
+  // get prompts with selected != true joined with sum of votes for each
+  const promptsWithVotes =
+    await db`SELECT prompts.*, COUNT(votes.id) AS votes FROM prompts LEFT JOIN votes ON prompts.id = votes.prompt WHERE prompts.selected IS NOT TRUE GROUP BY prompts.id ORDER BY votes DESC`
+
+  return (
+    <>
+      <table
+        border="0"
+        cellPadding="0"
+        cellSpacing="0"
+        style={{ maxWidth: 600 }}
+      >
+        <tbody>
+          {promptsWithVotes.map((prompt, i) => (
+            <tr key={i}>
+              <td width={30}>{i + 1}</td>
+              <td>
+                <span>{prompt.votes} points</span>
+                <br />
+                {logged ? (
+                  <UpvoteBtn id={prompt.id} />
+                ) : (
+                  <Link href="/login">
+                    <small>upvote</small>
+                  </Link>
+                )}
+              </td>
+              <td>
+                <pre style={{ maxWidth: 800 }}>{prompt.text}</pre>
+              </td>
+            </tr>
+          ))}
+        </tbody>
+      </table>
+    </>
+  )
+}

app/prompts/page.js

@@ -0,0 +1,34 @@
+import db from "@/utils/db"
+import Link from "next/link"
+
+export default async function Dataset() {
+  const prompts =
+    await db`SELECT * FROM prompts WHERE selected = true ORDER BY text ASC `
+
+  const types = Array.from(new Set(prompts.map((p) => p.type)))
+
+  return (
+    <>
+      {types.map((type, k) => (
+        <div key={k}>
+          <p>{type}:</p>
+          <br />
+          <ul>
+            {prompts
+              .filter((p) => p.type === type)
+              .map((prompt, i) => (
+                <li key={i}>
+                  <pre style={{ maxWidth: 800 }}>
+                    {prompt.text}
+                    <br />
+                    <br />
+                    <Link href={`/prompts/${prompt.slug}`}>results</Link>
+                  </pre>
+                </li>
+              ))}
+          </ul>
+        </div>
+      ))}
+    </>
+  )
+}

app/prompts/submit/page.js

@@ -0,0 +1,113 @@
+import db from "@/utils/db"
+import { cookies } from "next/headers"
+
+import Link from "next/link"
+import { redirect } from "next/navigation"
+
+import jwt from "jsonwebtoken"
+
+export default async function Submit() {
+  // Get user session token
+
+  async function create(formData) {
+    "use server"
+
+    const cookiesList = cookies()
+    const token = cookiesList.get("token")
+
+    console.log("token", token)
+
+    if (!token) throw new Error("not logged")
+
+    const { userId } = jwt.verify(token.value, process.env.JWT_SECRET)
+
+    const [userObj] = await db`SELECT * FROM users WHERE id = ${userId}`
+    if (!userObj) throw new Error("user not found")
+
+    const text = formData.get("prompt")
+    const slug = formData.get("slug")
+
+    if (text.length <= 20 || text.length > 1000)
+      throw new Error("prompt too long or too short")
+
+    const [prompt] =
+      await db`INSERT INTO prompts (text, submitter, slug) VALUES (${text}, ${userObj.id}, ${slug}) RETURNING *`
+
+    const [vote] =
+      await db`INSERT INTO votes ("user", prompt) VALUES (${userObj.id}, ${prompt.id}) RETURNING *`
+
+    redirect(`/prompts`)
+
+    // send email to user to confirm submission
+  }
+
+  return (
+    <>
+      <p>Submit a new prompt to be included to the benchmark.</p>
+      <p>
+        Each week, the highest rated prompt will become part of the benchmark.
+      </p>
+      <p>What makes a good prompt:</p>
+      <ul>
+        <li>
+          Can be broke down <Link href="/about">into rubrics</Link> & evaluated
+        </li>
+        <li>
+          Is original and not popular on the internet (unlikely to be already
+          part in the benchmark)
+        </li>
+        <li>Is not too long (max 1000 characters)</li>
+      </ul>
+      <br />
+
+      <form action={create}>
+        <table
+          id="hnmain"
+          border="0"
+          cellPadding="0"
+          cellSpacing="0"
+          style={{ maxWidth: 680 }}
+        >
+          <tbody>
+            <tr>
+              <td>
+                <label htmlFor="prompt">Prompt</label>
+              </td>
+              <td>
+                <textarea
+                  id="prompt"
+                  name="prompt"
+                  rows="10"
+                  minLength={20}
+                  maxLength={1000}
+                  cols="50"
+                  required
+                />
+              </td>
+            </tr>
+            <tr>
+              <td>
+                <label htmlFor="rubrics">Slug</label>
+              </td>
+              <td>
+                <input
+                  type="text"
+                  id="slug"
+                  name="slug"
+                  required
+                  pattern="^[a-z0-9]+(?:-[a-z0-9]+)*$"
+                />
+              </td>
+            </tr>
+          </tbody>
+        </table>
+        <br />
+        <p>
+          To prevent spam, you will receive an email to confirm your submission.
+        </p>
+
+        <button type="submit">Submit</button>
+      </form>
+    </>
+  )
+}

components/CaptchaInput.js

@@ -0,0 +1,21 @@
+"use client"
+import ReCAPTCHA from "react-google-recaptcha"
+import { useState } from "react"
+
+export default function CaptchaInput() {
+  const [recaptchaValue, setRecaptchaValue] = useState(null)
+
+  return (
+    <>
+      <input
+        type="hidden"
+        name="g-recaptcha-response"
+        value={recaptchaValue || ""}
+      />
+      <ReCAPTCHA
+        sitekey={process.env.NEXT_PUBLIC_RECAPTCHA_SITE_KEY}
+        onChange={(value) => setRecaptchaValue(value)}
+      />
+    </>
+  )
+}

components/SelectModels.js

@@ -0,0 +1,43 @@
+"use client"
+
+import { useEffect, useState } from "react"
+import { useRouter } from "next/navigation"
+
+export default function SelectModels({ models }) {
+  const router = useRouter()
+  const [model1, setModel1] = useState("")
+  const [model2, setModel2] = useState("")
+
+  useEffect(() => {
+    if (model1 && model2) {
+      router.push(`/compare/${model1}-vs-${model2}`)
+    }
+  }, [model1, model2])
+
+  return (
+    <div>
+      <select onChange={(e) => setModel1(e.target.value)}>
+        <option value="">Select model 1</option>
+        {models
+
+          .sort((a, b) => b.total_score - a.total_score)
+          .map((model, i) => (
+            <option key={i} value={model.slug}>
+              {model.name}
+            </option>
+          ))}
+      </select>
+      <select onChange={(e) => setModel2(e.target.value)}>
+        <option value="">Select model 2</option>
+        {models
+
+          .sort((a, b) => b.total_score - a.total_score)
+          .map((model, i) => (
+            <option key={i} value={model.slug}>
+              {model.name}
+            </option>
+          ))}
+      </select>
+    </div>
+  )
+}

components/UpvoteBtn.js

@@ -0,0 +1,20 @@
+"use client"
+
+import { useState } from "react"
+
+export default function UpvoteBtn({ id }) {
+  const [upvoted, setUpvoted] = useState(false)
+
+  const upvote = async (id) => {
+    const response = await fetch(`/api/upvote?prompt=${id}`)
+    const data = await response.text()
+    if (data === "ok") return setUpvoted(true)
+    alert(data)
+  }
+
+  return (
+    <a href="#" onClick={() => upvote(id)}>
+      <small>{upvoted ? "Upvoted" : "Upvote"}</small>
+    </a>
+  )
+}

next.config.js

@@ -2,6 +2,10 @@
 const nextConfig = {
   reactStrictMode: true,
   output: "standalone",
+
+  experimental: {
+    serverActions: true,
+  },
 }
 
 module.exports = nextConfig

package-lock.json

@@ -8,49 +8,82 @@
       "name": "benchmarks",
       "version": "0.1.0",
       "dependencies": {
-        "next": "13.4.19",
+        "@auth/pg-adapter": "^0.2.1",
+        "jsonwebtoken": "^9.0.2",
+        "next": "^13.5.4",
         "next-plausible": "^3.11.1",
         "openai": "^4.5.0",
-        "react": "18.2.0",
-        "react-dom": "18.2.0",
-        "sqlite": "^5.0.1",
-        "sqlite3": "^5.1.6"
+        "postgres": "^3.3.5",
+        "react": "^18.2.0",
+        "react-dom": "^18.2.0",
+        "react-google-recaptcha": "^3.1.0"
+      },
+      "devDependencies": {
+        "@types/react": "18.2.24",
+        "typescript": "5.2.2"
+      }
+    },
+    "node_modules/@auth/core": {
+      "version": "0.16.1",
+      "resolved": "https://registry.npmjs.org/@auth/core/-/core-0.16.1.tgz",
+      "integrity": "sha512-V+YifnjpyOadiiTbxfYDV2xYWo8xpKNtwYVskAEKUSwMvE0FlSlP+10QGBpf0axS/AJFOO61IR6GncFF/IOrHQ==",
+      "dependencies": {
+        "@panva/hkdf": "^1.0.4",
+        "cookie": "0.5.0",
+        "jose": "^4.11.1",
+        "oauth4webapi": "^2.0.6",
+        "preact": "10.11.3",
+        "preact-render-to-string": "5.2.3"
+      },
+      "peerDependencies": {
+        "nodemailer": "^6.8.0"
+      },
+      "peerDependenciesMeta": {
+        "nodemailer": {
+          "optional": true
+        }
       }
     },
-    "node_modules/@gar/promisify": {
-      "version": "1.1.3",
-      "resolved": "https://registry.npmjs.org/@gar/promisify/-/promisify-1.1.3.tgz",
-      "integrity": "sha512-k2Ty1JcVojjJFwrg/ThKi2ujJ7XNLYaFGNB/bWT9wGR+oSMJHMa5w+CUq6p/pVrKeNNgA7pCqEcjSnHVoqJQFw==",
-      "optional": true
+    "node_modules/@auth/core/node_modules/preact": {
+      "version": "10.11.3",
+      "resolved": "https://registry.npmjs.org/preact/-/preact-10.11.3.tgz",
+      "integrity": "sha512-eY93IVpod/zG3uMF22Unl8h9KkrcKIRs2EGar8hwLZZDU1lkjph303V9HZBwufh2s736U6VXuhD109LYqPoffg==",
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/preact"
+      }
     },
-    "node_modules/@mapbox/node-pre-gyp": {
-      "version": "1.0.11",
-      "resolved": "https://registry.npmjs.org/@mapbox/node-pre-gyp/-/node-pre-gyp-1.0.11.tgz",
-      "integrity": "sha512-Yhlar6v9WQgUp/He7BdgzOz8lqMQ8sU+jkCq7Wx8Myc5YFJLbEe7lgui/V7G1qB1DJykHSGwreceSaD60Y0PUQ==",
+    "node_modules/@auth/core/node_modules/preact-render-to-string": {
+      "version": "5.2.3",
+      "resolved": "https://registry.npmjs.org/preact-render-to-string/-/preact-render-to-string-5.2.3.tgz",
+      "integrity": "sha512-aPDxUn5o3GhWdtJtW0svRC2SS/l8D9MAgo2+AWml+BhDImb27ALf04Q2d+AHqUUOc6RdSXFIBVa2gxzgMKgtZA==",
       "dependencies": {
-        "detect-libc": "^2.0.0",
-        "https-proxy-agent": "^5.0.0",
-        "make-dir": "^3.1.0",
-        "node-fetch": "^2.6.7",
-        "nopt": "^5.0.0",
-        "npmlog": "^5.0.1",
-        "rimraf": "^3.0.2",
-        "semver": "^7.3.5",
-        "tar": "^6.1.11"
+        "pretty-format": "^3.8.0"
       },
-      "bin": {
-        "node-pre-gyp": "bin/node-pre-gyp"
+      "peerDependencies": {
+        "preact": ">=10"
+      }
+    },
+    "node_modules/@auth/pg-adapter": {
+      "version": "0.2.1",
+      "resolved": "https://registry.npmjs.org/@auth/pg-adapter/-/pg-adapter-0.2.1.tgz",
+      "integrity": "sha512-Yp8fMORE44fP2vAqDCa4SvDKV+HE7z/6cQIlLmwbWJd8ywnrgWKE/TvqUARZ1o3+ITx9d683CZeyexdefKw5Yw==",
+      "dependencies": {
+        "@auth/core": "0.16.1"
+      },
+      "peerDependencies": {
+        "pg": "^8"
       }
     },
     "node_modules/@next/env": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/env/-/env-13.4.19.tgz",
-      "integrity": "sha512-FsAT5x0jF2kkhNkKkukhsyYOrRqtSxrEhfliniIq0bwWbuXLgyt3Gv0Ml+b91XwjwArmuP7NxCiGd++GGKdNMQ=="
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/env/-/env-13.5.4.tgz",
+      "integrity": "sha512-LGegJkMvRNw90WWphGJ3RMHMVplYcOfRWf2Be3td3sUa+1AaxmsYyANsA+znrGCBjXJNi4XAQlSoEfUxs/4kIQ=="
     },
     "node_modules/@next/swc-darwin-arm64": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-13.4.19.tgz",
-      "integrity": "sha512-vv1qrjXeGbuF2mOkhkdxMDtv9np7W4mcBtaDnHU+yJG+bBwa6rYsYSCI/9Xm5+TuF5SbZbrWO6G1NfTh1TMjvQ==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-13.5.4.tgz",
+      "integrity": "sha512-Df8SHuXgF1p+aonBMcDPEsaahNo2TCwuie7VXED4FVyECvdXfRT9unapm54NssV9tF3OQFKBFOdlje4T43VO0w==",
       "cpu": [
         "arm64"
       ],
@@ -63,9 +96,9 @@
       }
     },
     "node_modules/@next/swc-darwin-x64": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-13.4.19.tgz",
-      "integrity": "sha512-jyzO6wwYhx6F+7gD8ddZfuqO4TtpJdw3wyOduR4fxTUCm3aLw7YmHGYNjS0xRSYGAkLpBkH1E0RcelyId6lNsw==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-13.5.4.tgz",
+      "integrity": "sha512-siPuUwO45PnNRMeZnSa8n/Lye5ZX93IJom9wQRB5DEOdFrw0JjOMu1GINB8jAEdwa7Vdyn1oJ2xGNaQpdQQ9Pw==",
       "cpu": [
         "x64"
       ],
@@ -78,9 +111,9 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-gnu": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-13.4.19.tgz",
-      "integrity": "sha512-vdlnIlaAEh6H+G6HrKZB9c2zJKnpPVKnA6LBwjwT2BTjxI7e0Hx30+FoWCgi50e+YO49p6oPOtesP9mXDRiiUg==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-13.5.4.tgz",
+      "integrity": "sha512-l/k/fvRP/zmB2jkFMfefmFkyZbDkYW0mRM/LB+tH5u9pB98WsHXC0WvDHlGCYp3CH/jlkJPL7gN8nkTQVrQ/2w==",
       "cpu": [
         "arm64"
       ],
@@ -93,9 +126,9 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-musl": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-13.4.19.tgz",
-      "integrity": "sha512-aU0HkH2XPgxqrbNRBFb3si9Ahu/CpaR5RPmN2s9GiM9qJCiBBlZtRTiEca+DC+xRPyCThTtWYgxjWHgU7ZkyvA==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-13.5.4.tgz",
+      "integrity": "sha512-YYGb7SlLkI+XqfQa8VPErljb7k9nUnhhRrVaOdfJNCaQnHBcvbT7cx/UjDQLdleJcfyg1Hkn5YSSIeVfjgmkTg==",
       "cpu": [
         "arm64"
       ],
@@ -108,9 +141,9 @@
       }
     },
     "node_modules/@next/swc-linux-x64-gnu": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-13.4.19.tgz",
-      "integrity": "sha512-htwOEagMa/CXNykFFeAHHvMJeqZfNQEoQvHfsA4wgg5QqGNqD5soeCer4oGlCol6NGUxknrQO6VEustcv+Md+g==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-13.5.4.tgz",
+      "integrity": "sha512-uE61vyUSClnCH18YHjA8tE1prr/PBFlBFhxBZis4XBRJoR+txAky5d7gGNUIbQ8sZZ7LVkSVgm/5Fc7mwXmRAg==",
       "cpu": [
         "x64"
       ],
@@ -123,9 +156,9 @@
       }
     },
     "node_modules/@next/swc-linux-x64-musl": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-13.4.19.tgz",
-      "integrity": "sha512-4Gj4vvtbK1JH8ApWTT214b3GwUh9EKKQjY41hH/t+u55Knxi/0wesMzwQRhppK6Ddalhu0TEttbiJ+wRcoEj5Q==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-13.5.4.tgz",
+      "integrity": "sha512-qVEKFYML/GvJSy9CfYqAdUexA6M5AklYcQCW+8JECmkQHGoPxCf04iMh7CPR7wkHyWWK+XLt4Ja7hhsPJtSnhg==",
       "cpu": [
         "x64"
       ],
@@ -138,9 +171,9 @@
       }
     },
     "node_modules/@next/swc-win32-arm64-msvc": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-13.4.19.tgz",
-      "integrity": "sha512-bUfDevQK4NsIAHXs3/JNgnvEY+LRyneDN788W2NYiRIIzmILjba7LaQTfihuFawZDhRtkYCv3JDC3B4TwnmRJw==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-13.5.4.tgz",
+      "integrity": "sha512-mDSQfqxAlfpeZOLPxLymZkX0hYF3juN57W6vFHTvwKlnHfmh12Pt7hPIRLYIShk8uYRsKPtMTth/EzpwRI+u8w==",
       "cpu": [
         "arm64"
       ],
@@ -153,9 +186,9 @@
       }
     },
     "node_modules/@next/swc-win32-ia32-msvc": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-13.4.19.tgz",
-      "integrity": "sha512-Y5kikILFAr81LYIFaw6j/NrOtmiM4Sf3GtOc0pn50ez2GCkr+oejYuKGcwAwq3jiTKuzF6OF4iT2INPoxRycEA==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-13.5.4.tgz",
+      "integrity": "sha512-aoqAT2XIekIWoriwzOmGFAvTtVY5O7JjV21giozBTP5c6uZhpvTWRbmHXbmsjZqY4HnEZQRXWkSAppsIBweKqw==",
       "cpu": [
         "ia32"
       ],
@@ -168,9 +201,9 @@
       }
     },
     "node_modules/@next/swc-win32-x64-msvc": {
-      "version": "13.4.19",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-13.4.19.tgz",
-      "integrity": "sha512-YzA78jBDXMYiINdPdJJwGgPNT3YqBNNGhsthsDoWHL9p24tEJn9ViQf/ZqTbwSpX/RrkPupLfuuTH2sf73JBAw==",
+      "version": "13.5.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-13.5.4.tgz",
+      "integrity": "sha512-cyRvlAxwlddlqeB9xtPSfNSCRy8BOa4wtMo0IuI9P7Y0XT2qpDrpFKRyZ7kUngZis59mPVla5k8X1oOJ8RxDYg==",
       "cpu": [
         "x64"
       ],
@@ -182,47 +215,22 @@
         "node": ">= 10"
       }
     },
-    "node_modules/@npmcli/fs": {
+    "node_modules/@panva/hkdf": {
       "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/@npmcli/fs/-/fs-1.1.1.tgz",
-      "integrity": "sha512-8KG5RD0GVP4ydEzRn/I4BNDuxDtqVbOdm8675T49OIG/NGhaK0pjPX7ZcDlvKYbA+ulvVK3ztfcF4uBdOxuJbQ==",
-      "optional": true,
-      "dependencies": {
-        "@gar/promisify": "^1.0.1",
-        "semver": "^7.3.5"
-      }
-    },
-    "node_modules/@npmcli/move-file": {
-      "version": "1.1.2",
-      "resolved": "https://registry.npmjs.org/@npmcli/move-file/-/move-file-1.1.2.tgz",
-      "integrity": "sha512-1SUf/Cg2GzGDyaf15aR9St9TWlb+XvbZXWpDx8YKs7MLzMH/BCeopv+y9vzrzgkfykCGuWOlSu3mZhj2+FQcrg==",
-      "deprecated": "This functionality has been moved to @npmcli/fs",
-      "optional": true,
-      "dependencies": {
-        "mkdirp": "^1.0.4",
-        "rimraf": "^3.0.2"
-      },
-      "engines": {
-        "node": ">=10"
+      "resolved": "https://registry.npmjs.org/@panva/hkdf/-/hkdf-1.1.1.tgz",
+      "integrity": "sha512-dhPeilub1NuIG0X5Kvhh9lH4iW3ZsHlnzwgwbOlgwQ2wG1IqFzsgHqmKPk3WzsdWAeaxKJxgM0+W433RmN45GA==",
+      "funding": {
+        "url": "https://github.com/sponsors/panva"
       }
     },
     "node_modules/@swc/helpers": {
-      "version": "0.5.1",
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+        "react-is": "^16.13.1"
       }
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     "node_modules/react": {
@@ -1324,6 +1010,18 @@
         "node": ">=0.10.0"
       }
     },
+    "node_modules/react-async-script": {
+      "version": "1.2.0",
+      "resolved": "https://registry.npmjs.org/react-async-script/-/react-async-script-1.2.0.tgz",
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+      "dependencies": {
+        "hoist-non-react-statics": "^3.3.0",
+        "prop-types": "^15.5.0"
+      },
+      "peerDependencies": {
+        "react": ">=16.4.1"
+      }
+    },
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       "version": "18.2.0",
       "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-18.2.0.tgz",
@@ -1336,41 +1034,22 @@
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+    "node_modules/react-google-recaptcha": {
+      "version": "3.1.0",
+      "resolved": "https://registry.npmjs.org/react-google-recaptcha/-/react-google-recaptcha-3.1.0.tgz",
+      "integrity": "sha512-cYW2/DWas8nEKZGD7SCu9BSuVz8iOcOLHChHyi7upUuVhkpkhYG/6N3KDiTQ3XAiZ2UAZkfvYKMfAHOzBOcGEg==",
       "dependencies": {
-        "inherits": "^2.0.3",
-        "string_decoder": "^1.1.1",
-        "util-deprecate": "^1.0.1"
+        "prop-types": "^15.5.0",
+        "react-async-script": "^1.2.0"
       },
-      "engines": {
-        "node": ">= 6"
-      }
-    },
-    "node_modules/retry": {
-      "version": "0.12.0",
-      "resolved": "https://registry.npmjs.org/retry/-/retry-0.12.0.tgz",
-      "integrity": "sha512-9LkiTwjUh6rT555DtE9rTX+BKByPfrMzEAtnlEtdEwr3Nkffwiihqe2bWADg+OQRjt9gl6ICdmB/ZFDCGAtSow==",
-      "optional": true,
-      "engines": {
-        "node": ">= 4"
+      "peerDependencies": {
+        "react": ">=16.4.1"
       }
     },
-    "node_modules/rimraf": {
-      "version": "3.0.2",
-      "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-3.0.2.tgz",
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-      },
-      "bin": {
-        "rimraf": "bin.js"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/isaacs"
-      }
+    "node_modules/react-is": {
+      "version": "16.13.1",
+      "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
+      "integrity": "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="
     },
     "node_modules/safe-buffer": {
       "version": "5.2.1",
@@ -1395,7 +1074,8 @@
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       "resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz",
       "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==",
-      "optional": true
+      "optional": true,
+      "peer": true
     },
     "node_modules/scheduler": {
       "version": "0.23.0",
@@ -1419,54 +1099,6 @@
         "node": ">=10"
       }
     },
-    "node_modules/set-blocking": {
-      "version": "2.0.0",
-      "resolved": "https://registry.npmjs.org/set-blocking/-/set-blocking-2.0.0.tgz",
-      "integrity": "sha512-KiKBS8AnWGEyLzofFfmvKwpdPzqiy16LvQfK3yv/fVH7Bj13/wl3JSR1J+rfgRE9q7xUJK4qvgS8raSOeLUehw=="
-    },
-    "node_modules/signal-exit": {
-      "version": "3.0.7",
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-    },
-    "node_modules/smart-buffer": {
-      "version": "4.2.0",
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-      "optional": true,
-      "engines": {
-        "node": ">= 6.0.0",
-        "npm": ">= 3.0.0"
-      }
-    },
-    "node_modules/socks": {
-      "version": "2.7.1",
-      "resolved": "https://registry.npmjs.org/socks/-/socks-2.7.1.tgz",
-      "integrity": "sha512-7maUZy1N7uo6+WVEX6psASxtNlKaNVMlGQKkG/63nEDdLOWNbiUMoLK7X4uYoLhQstau72mLgfEWcXcwsaHbYQ==",
-      "optional": true,
-      "dependencies": {
-        "ip": "^2.0.0",
-        "smart-buffer": "^4.2.0"
-      },
-      "engines": {
-        "node": ">= 10.13.0",
-        "npm": ">= 3.0.0"
-      }
-    },
-    "node_modules/socks-proxy-agent": {
-      "version": "6.2.1",
-      "resolved": "https://registry.npmjs.org/socks-proxy-agent/-/socks-proxy-agent-6.2.1.tgz",
-      "integrity": "sha512-a6KW9G+6B3nWZ1yB8G7pJwL3ggLy1uTzKAgCb7ttblwqdz9fMGJUuTy3uFzEP48FAs9FLILlmzDlE2JJhVQaXQ==",
-      "optional": true,
-      "dependencies": {
-        "agent-base": "^6.0.2",
-        "debug": "^4.3.3",
-        "socks": "^2.6.2"
-      },
-      "engines": {
-        "node": ">= 10"
-      }
-    },
     "node_modules/source-map-js": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.0.2.tgz",
@@ -1475,43 +1107,13 @@
         "node": ">=0.10.0"
       }
     },
-    "node_modules/sqlite": {
-      "version": "5.0.1",
-      "resolved": "https://registry.npmjs.org/sqlite/-/sqlite-5.0.1.tgz",
-      "integrity": "sha512-sRxbVwzNX9rvEPnxz9scNqi60KtfxBXMzzus5BI4+0nPtVkyEXyBwQ7JkM2M1CiVvEFw6s1vn2Z9/1DEdSpiaA=="
-    },
-    "node_modules/sqlite3": {
-      "version": "5.1.6",
-      "resolved": "https://registry.npmjs.org/sqlite3/-/sqlite3-5.1.6.tgz",
-      "integrity": "sha512-olYkWoKFVNSSSQNvxVUfjiVbz3YtBwTJj+mfV5zpHmqW3sELx2Cf4QCdirMelhM5Zh+KDVaKgQHqCxrqiWHybw==",
-      "hasInstallScript": true,
-      "dependencies": {
-        "@mapbox/node-pre-gyp": "^1.0.0",
-        "node-addon-api": "^4.2.0",
-        "tar": "^6.1.11"
-      },
-      "optionalDependencies": {
-        "node-gyp": "8.x"
-      },
-      "peerDependencies": {
-        "node-gyp": "8.x"
-      },
-      "peerDependenciesMeta": {
-        "node-gyp": {
-          "optional": true
-        }
-      }
-    },
-    "node_modules/ssri": {
-      "version": "8.0.1",
-      "resolved": "https://registry.npmjs.org/ssri/-/ssri-8.0.1.tgz",
-      "integrity": "sha512-97qShzy1AiyxvPNIkLWoGua7xoQzzPjQ0HAH4B0rWKo7SZ6USuPcrUiAFrws0UH8RrbWmgq3LMTObhPIHbbBeQ==",
-      "optional": true,
-      "dependencies": {
-        "minipass": "^3.1.1"
-      },
+    "node_modules/split2": {
+      "version": "4.2.0",
+      "resolved": "https://registry.npmjs.org/split2/-/split2-4.2.0.tgz",
+      "integrity": "sha512-UcjcJOWknrNkF6PLX83qcHM6KHgVKNkV62Y8a5uYDVv9ydGQVwAHMKqHdJje1VTWpljG0WYpCDhrCdAOYH4TWg==",
+      "peer": true,
       "engines": {
-        "node": ">= 8"
+        "node": ">= 10.x"
       }
     },
     "node_modules/streamsearch": {
@@ -1522,38 +1124,6 @@
         "node": ">=10.0.0"
       }
     },
-    "node_modules/string_decoder": {
-      "version": "1.3.0",
-      "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz",
-      "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==",
-      "dependencies": {
-        "safe-buffer": "~5.2.0"
-      }
-    },
-    "node_modules/string-width": {
-      "version": "4.2.3",
-      "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
-      "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
-      "dependencies": {
-        "emoji-regex": "^8.0.0",
-        "is-fullwidth-code-point": "^3.0.0",
-        "strip-ansi": "^6.0.1"
-      },
-      "engines": {
-        "node": ">=8"
-      }
-    },
-    "node_modules/strip-ansi": {
-      "version": "6.0.1",
-      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
-      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
-      "dependencies": {
-        "ansi-regex": "^5.0.1"
-      },
-      "engines": {
-        "node": ">=8"
-      }
-    },
     "node_modules/styled-jsx": {
       "version": "5.1.1",
       "resolved": "https://registry.npmjs.org/styled-jsx/-/styled-jsx-5.1.1.tgz",
@@ -1576,30 +1146,6 @@
         }
       }
     },
-    "node_modules/tar": {
-      "version": "6.2.0",
-      "resolved": "https://registry.npmjs.org/tar/-/tar-6.2.0.tgz",
-      "integrity": "sha512-/Wo7DcT0u5HUV486xg675HtjNd3BXZ6xDbzsCUZPt5iw8bTQ63bP0Raut3mvro9u+CUyq7YQd8Cx55fsZXxqLQ==",
-      "dependencies": {
-        "chownr": "^2.0.0",
-        "fs-minipass": "^2.0.0",
-        "minipass": "^5.0.0",
-        "minizlib": "^2.1.1",
-        "mkdirp": "^1.0.3",
-        "yallist": "^4.0.0"
-      },
-      "engines": {
-        "node": ">=10"
-      }
-    },
-    "node_modules/tar/node_modules/minipass": {
-      "version": "5.0.0",
-      "resolved": "https://registry.npmjs.org/minipass/-/minipass-5.0.0.tgz",
-      "integrity": "sha512-3FnjYuehv9k6ovOEbyOswadCDPX1piCfhV8ncmYtHOjuPwylVWsghTLo7rabjC3Rx5xD4HDx8Wm1xnMF7S5qFQ==",
-      "engines": {
-        "node": ">=8"
-      }
-    },
     "node_modules/tr46": {
       "version": "0.0.3",
       "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz",
@@ -1610,29 +1156,19 @@
       "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.2.tgz",
       "integrity": "sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q=="
     },
-    "node_modules/unique-filename": {
-      "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/unique-filename/-/unique-filename-1.1.1.tgz",
-      "integrity": "sha512-Vmp0jIp2ln35UTXuryvjzkjGdRyf9b2lTXuSYUiPmzRcl3FDtYqAwOnTJkAngD9SWhnoJzDbTKwaOrZ+STtxNQ==",
-      "optional": true,
-      "dependencies": {
-        "unique-slug": "^2.0.0"
-      }
-    },
-    "node_modules/unique-slug": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/unique-slug/-/unique-slug-2.0.2.tgz",
-      "integrity": "sha512-zoWr9ObaxALD3DOPfjPSqxt4fnZiWblxHIgeWqW8x7UqDzEtHEQLzji2cuJYQFCU6KmoJikOYAZlrTHHebjx2w==",
-      "optional": true,
-      "dependencies": {
-        "imurmurhash": "^0.1.4"
+    "node_modules/typescript": {
+      "version": "5.2.2",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.2.2.tgz",
+      "integrity": "sha512-mI4WrpHsbCIcwT9cF4FZvr80QUeKvsUsUvKDoR+X/7XHQH98xYD8YHZg7ANtz2GtZt/CBq2QJ0thkGJMHfqc1w==",
+      "dev": true,
+      "bin": {
+        "tsc": "bin/tsc",
+        "tsserver": "bin/tsserver"
+      },
+      "engines": {
+        "node": ">=14.17"
       }
     },
-    "node_modules/util-deprecate": {
-      "version": "1.0.2",
-      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
-      "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="
-    },
     "node_modules/watchpack": {
       "version": "2.4.0",
       "resolved": "https://registry.npmjs.org/watchpack/-/watchpack-2.4.0.tgz",
@@ -1667,46 +1203,19 @@
         "webidl-conversions": "^3.0.0"
       }
     },
-    "node_modules/which": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
-      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
-      "optional": true,
-      "dependencies": {
-        "isexe": "^2.0.0"
-      },
-      "bin": {
-        "node-which": "bin/node-which"
-      },
+    "node_modules/xtend": {
+      "version": "4.0.2",
+      "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
+      "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==",
+      "peer": true,
       "engines": {
-        "node": ">= 8"
+        "node": ">=0.4"
       }
     },
-    "node_modules/wide-align": {
-      "version": "1.1.5",
-      "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.5.tgz",
-      "integrity": "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==",
-      "dependencies": {
-        "string-width": "^1.0.2 || 2 || 3 || 4"
-      }
-    },
-    "node_modules/wrappy": {
-      "version": "1.0.2",
-      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
-      "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
-    },
     "node_modules/yallist": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
       "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="
-    },
-    "node_modules/zod": {
-      "version": "3.21.4",
-      "resolved": "https://registry.npmjs.org/zod/-/zod-3.21.4.tgz",
-      "integrity": "sha512-m46AKbrzKVzOzs/DZgVnG5H55N1sv1M8qZU3A8RIKbs3mrACDNeIOeilDymVb2HdmP8uwshOCF4uJ8uM9rCqJw==",
-      "funding": {
-        "url": "https://github.com/sponsors/colinhacks"
-      }
     }
   }
 }

package.json

@@ -9,15 +9,21 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "next": "13.4.19",
+    "@auth/pg-adapter": "^0.2.1",
+    "jsonwebtoken": "^9.0.2",
+    "next": "^13.5.4",
     "next-plausible": "^3.11.1",
     "openai": "^4.5.0",
-    "react": "18.2.0",
-    "react-dom": "18.2.0",
-    "sqlite": "^5.0.1",
-    "sqlite3": "^5.1.6"
+    "postgres": "^3.3.5",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-google-recaptcha": "^3.1.0"
   },
   "prettier": {
     "semi": false
+  },
+  "devDependencies": {
+    "@types/react": "18.2.24",
+    "typescript": "5.2.2"
   }
 }

pages/_app.js

@@ -1,40 +0,0 @@
-import PlausibleProvider from "next-plausible"
-
-import "@/styles/globals.css"
-
-import Script from "next/script"
-
-export default function App({ Component, pageProps }) {
-  return (
-    <PlausibleProvider
-      domain="benchmarks.llmonitor.com"
-      scriptProps={{
-        src: "https://llmonitor.com/p/js/script.js",
-        // @ts-ignore
-        "data-api": "https://llmonitor.com/p/event",
-      }}
-      customDomain="benchmarks.llmonitor.com"
-    >
-      <Component {...pageProps} />
-      <footer>
-        <br />
-        <p>
-          Credit:{" "}
-          <a href="https://twitter.com/vincelwt" target="_blank">
-            @vincelwt
-          </a>
-        </p>
-
-        <a href="https://llmonitor.com" className="llmonitor" target="_blank">
-          by 📈 llmonitor
-        </a>
-
-        {/* Support embedding into HuggingFace */}
-        <Script
-          strategy="afterInteractive"
-          src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"
-        />
-      </footer>
-    </PlausibleProvider>
-  )
-}

pages/_document.js

@@ -1,13 +0,0 @@
-import { Html, Head, Main, NextScript } from 'next/document'
-
-export default function Document() {
-  return (
-    <Html lang="en">
-      <Head />
-      <body>
-        <Main />
-        <NextScript />
-      </body>
-    </Html>
-  )
-}

pages/index.js

@@ -1,186 +0,0 @@
-import getDB from "@/utils/getDB"
-import Head from "next/head"
-import Link from "next/link"
-import { useRouter } from "next/router"
-import { useEffect, useMemo, useState } from "react"
-
-// import styles from '@/styles/Home.module.css'
-
-export const getStaticProps = async () => {
-  const db = await getDB()
-
-  const prompts = await db.all(`SELECT * FROM prompts ORDER BY text ASC`)
-
-  // get all models that have at least 1 result
-  const models = await db.all(
-    `SELECT * FROM models WHERE id IN (SELECT DISTINCT model FROM results) ORDER BY name ASC`
-  )
-
-  return { props: { prompts, models } }
-}
-
-export default function Home({ prompts, models }) {
-  const router = useRouter()
-
-  const [viewBy, setViewBy] = useState(router.query.viewBy || "prompt")
-
-  const changeView = (viewBy) => {
-    router.push({ query: { viewBy } })
-  }
-
-  useEffect(() => {
-    if (router.query.viewBy) setViewBy(router.query.viewBy)
-  }, [router.query.viewBy])
-
-  const types = useMemo(() => {
-    return Array.from(new Set(prompts.map((p) => p.type)))
-  }, [prompts])
-
-  return (
-    <>
-      <Head>
-        <title>LLM Benchmarks</title>
-        <meta
-          name="description"
-          content="Human-readable benchmarks of 60+ open-source and proprietary LLMs."
-        />
-        <meta name="viewport" content="width=device-width, initial-scale=1" />
-      </Head>
-      <main>
-        <h1>Crowdsourced LLM Benchmark</h1>
-        <br />
-        <p>
-          Benchmarks like HellaSwag are a bit too abstract for me to get a sense
-          of how well they perform in real-world workflows.
-        </p>
-        <br />
-
-        <p>
-          I had the idea of writing a script that asks prompts testing basic
-          reasoning, instruction following, and creativity on around 60 models
-          that I could get my hands on through inferences API.
-        </p>
-        <br />
-        <p>
-          The script stored all the answers in a SQLite database, and those are
-          the raw results.
-        </p>
-        <br />
-        <br />
-        <p>
-          {`view: `}
-          <a href="#" onClick={() => changeView("model")}>
-            models
-          </a>{" "}
-          /
-          <a href="#" onClick={() => changeView("prompt")}>
-            prompts
-          </a>{" "}
-        </p>
-        <br />
-        {viewBy === "prompt" ? (
-          <>
-            {types.map((type, k) => (
-              <div key={k}>
-                <p>{type}:</p>
-                <br />
-                <ul>
-                  {prompts
-                    .filter((p) => p.type === type)
-                    .map((prompt, i) => (
-                      <li key={i}>
-                        <pre style={{ maxWidth: 800 }}>
-                          {prompt.text}
-                          <br />
-                          <br />
-                          <Link href={`/${prompt.slug}`}>results</Link>
-                        </pre>
-                      </li>
-                    ))}
-                </ul>
-              </div>
-            ))}
-          </>
-        ) : (
-          <ul>
-            {models
-              .score((s) => s.score)
-              .map((model, i) => (
-                <li key={i}>
-                  {model.name} -{" "}
-                  <Link
-                    href={`/model/${model.api_id
-                      .split("/")
-                      .pop()
-                      .toLowerCase()}`}
-                  >
-                    results
-                  </Link>{" "}
-                  - score: {model.score}
-                </li>
-              ))}
-          </ul>
-        )}
-        <br />
-        <br />
-        <h3>Notes</h3>
-        <br />
-        <ul>
-          <li>
-            I used a temperature of 0 and a max token limit of 240 for each test
-            (that's why a lot of answers are cropped). The rest are default
-            settings.
-          </li>
-          <li>
-            I made this with a mix of APIs from OpenRouter, TogetherAI, OpenAI,
-            Cohere, Aleph Alpha & AI21.
-          </li>
-          <li>
-            <b>This is imperfect.</b> I want to improve this by using better
-            stop sequences and prompt formatting tailored to each model. But
-            hopefully it can already make picking models a bit easier.
-          </li>
-          <li>
-            Ideas for the future: public votes to compute an ELO rating, compare
-            2 models side by side, community-submitted prompts (open to
-            suggestions)
-          </li>
-          <li>
-            Prompt suggestions, feedback or say hi: vince [at] llmonitor.com
-          </li>
-          <li>
-            {`Shameless plug: I'm building an `}
-            <a href="https://github.com/llmonitor/llmonitor" target="_blank">
-              open-source observability tool for AI devs.
-            </a>
-          </li>
-        </ul>
-        <br />
-        <br />
-        <table style={{ maxWidth: 600 }}>
-          <th>
-            <p>
-              Edit: as this got popular, I added an email form to receive
-              notifications for future benchmark results:
-            </p>
-            <iframe
-              src="https://embeds.beehiiv.com/65bd6af1-2dea-417a-baf2-b65bc27e1610?slim=true"
-              height="52"
-              frameborder="0"
-              scrolling="no"
-              style={{
-                width: 400,
-                border: "none",
-                transform: "scale(0.8)",
-                transformOrigin: "left",
-              }}
-            ></iframe>
-            <br />
-            <small>(no spam, max 1 email per month)</small>
-          </th>
-        </table>
-        <br />
-      </main>
-    </>
-  )
-}

pages/model/[id].js

@@ -1,109 +0,0 @@
-import getDB from "@/utils/getDB"
-import Head from "next/head"
-import Link from "next/link"
-import { useMemo } from "react"
-
-export const getStaticPaths = async () => {
-  const db = await getDB()
-
-  const models = await db.all(`SELECT * FROM models`)
-
-  return {
-    paths: models.map((model) => ({
-      params: { id: model.api_id.split("/").pop().toLowerCase() },
-    })),
-    fallback: false,
-  }
-}
-
-export const getStaticProps = async (props) => {
-  const db = await getDB()
-
-  const { id } = props.params
-
-  console.log("id", id)
-
-  // where api_id contains the id (it's in lowercase)
-  const model = await db.get(`SELECT * FROM models WHERE api_id LIKE ?`, [
-    `%${id}%`,
-  ])
-
-  // get all results for this model, with their prompt (join)
-  const results = await db.all(
-    `SELECT * FROM results INNER JOIN prompts ON results.prompt = prompts.id WHERE model = ? ORDER BY prompts.text DESC`,
-    [model.id]
-  )
-
-  return { props: { results, model } }
-}
-
-export default function Prompt({ model, results }) {
-  const medianRate = useMemo(() => {
-    const rates = results.map((r) => r.rate)
-    const sorted = rates.sort((a, b) => a - b)
-    const mid = Math.floor(sorted.length / 2)
-    return sorted.length % 2 !== 0
-      ? sorted[mid]
-      : (sorted[mid - 1] + sorted[mid]) / 2
-  }, [results])
-
-  return (
-    <>
-      <Head>
-        <title>
-          {model.org} {model.name} benchmark across 20 prompts
-        </title>
-        <meta
-          name="description"
-          content={`Human-readable benchmark of ${model.org} ${model.name} across 20 prompts.`}
-        />
-        <meta name="viewport" content="width=device-width, initial-scale=1" />
-      </Head>
-      <h3>
-        {model.org} {model.name}
-      </h3>
-      <p>Median output rate: {medianRate.toFixed(2)} chars / s</p>
-
-      <br />
-      <Link href="/">Back to home</Link>
-      <br />
-      <br />
-      <table>
-        <thead>
-          <tr>
-            <th>Prompt</th>
-            <th>Answer</th>
-            <th>Latency</th>
-          </tr>
-        </thead>
-        <tbody>
-          {results
-            .sort((a, b) => a.name > b.name)
-            .map((result) => (
-              <tr>
-                <td>
-                  <pre>
-                    {result.text}
-                    <br />
-                    <br />
-                    <Link href={`/${result.slug}`}>all answers</Link>
-                  </pre>
-                </td>
-                <td>
-                  <pre>{result.result.trim()}</pre>
-                </td>
-                <td>{result.duration}ms</td>
-              </tr>
-            ))}
-        </tbody>
-      </table>
-      <br />
-      <Link href="/">Back to home</Link>
-      <style jsx>{`
-        th:nth-child(1) {
-          width: 30%;
-        }
-      `}</style>
-    </>
-  )
-}

run/.gitignore

@@ -153,3 +153,5 @@ dmypy.json
 
 # Cython debug symbols
 cython_debug/
+
+cookies/

run/pyproject.toml

@@ -0,0 +1,22 @@
+[tool.poetry]
+name = "benchmarks"
+version = "0.1.0"
+description = ""
+authors = ["vincelwt <vince@llmonitor.com>"]
+
+
+[tool.poetry.dependencies]
+python = "^3.9"
+openai = "^0.28.1"
+requests = "^2.31.0"
+python-dotenv = "^1.0.0"
+termcolor = "^2.3.0"
+llmonitor = "0.0.11"
+hugchat = {git = "https://github.com/Soulter/hugging-chat-api", rev = "master"}
+psycopg2-binary = "^2.9.9"
+anthropic = "^0.3.11"
+tenacity = "^8.2.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

run/queriers.py

@@ -2,23 +2,73 @@ import openai
 import os
 import json
 import requests
-from dotenv import load_dotenv
-
 from llmonitor import monitor
+from hugchat import hugchat
+from hugchat.login import Login
+from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    wait_random_exponential,
+)  # for exponential backoff
 
+from dotenv import load_dotenv
 load_dotenv()
 
+
 TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
 COHERE_API_KEY = os.getenv('COHERE_API_KEY')
 AI21_API_KEY = os.getenv('AI21_API_KEY')
 ALEPH_API_KEY = os.getenv('ALEPH_API_KEY')
 OPEN_ROUTER_API_KEY = os.getenv('OPEN_ROUTER_API_KEY')
 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY')
 
-MAX_TOKENS = 300
+# Huggingface login credentials
+HUGGING_EMAIL = os.environ.get("HUGGING_EMAIL")
+HUGGING_PASSWORD = os.environ.get("HUGGING_PASSWORD")
+
+MAX_TOKENS = 600
 
 monitor(openai)
 
+
+# Log in to huggingface and grant authorization to huggingchat
+sign = Login(HUGGING_EMAIL, HUGGING_PASSWORD)
+cookie_path_dir = "./cookies"
+
+try:
+  cookies = sign.loadCookiesFromDir(cookie_path_dir) # This will detect if the JSON file exists, return cookies if it does and raise an Exception if it's not.
+
+except Exception as e:
+  print(e)
+  
+  # Save cookies to the local directory
+  sign.saveCookiesToDir(cookie_path_dir)
+  cookies = sign.login()
+
+chatbot = hugchat.ChatBot(cookies=cookies.get_dict())  # or cookie_path="usercookies/<email>.json"
+
+def hugchat_func(model, params):
+
+    # Create a new conversation
+    id = chatbot.new_conversation()
+    chatbot.change_conversation(id)
+
+    # get index from chatbot.llms of the model
+    index = [i for i, x in enumerate(chatbot.llms) if x == model['api_id']][0]
+
+    print(f"Switching to {index}")
+
+    # set the chatbot to the model
+    chatbot.switch_llm(index)
+
+    query_result = chatbot.query(params['text'], temperature=0, max_new_tokens=MAX_TOKENS, stop=params['stop'] if params.get('stop') else None)
+    
+    return query_result['text']
+
 def together(model, params):
     def format_prompt(prompt, prompt_type):
       if prompt_type == "language":
@@ -37,7 +87,7 @@ def together(model, params):
     data = {
         "model": model['api_id'],
         "prompt": format_prompt(params['text'], model['type']),
-        "stop": params['stop'] if model['type'] == "chat" else params.get('stop', None),
+        "stop": "\n<human>" if model['type'] == "chat" else params.get('stop', None),
         "temperature": 0,
         "max_tokens": MAX_TOKENS,
     }
@@ -71,6 +121,7 @@ def cohere(model, params):
 
     return json_response['generations'][0]['text']
 
+@retry(wait=wait_exponential(multiplier=1, min=4, max=16))
 def openai_func(model, params):
     
     openai.api_key = OPENAI_API_KEY
@@ -105,19 +156,39 @@ def ai21(model, params):
     return json_response['completions'][0]['data']['text']
 
 def openrouter(model, params):
-    openai.api_key = OPEN_ROUTER_API_KEY
-    openai.api_base ="https://openrouter.ai/api/v1"
-    
-    completion = openai.ChatCompletion.create(
-        messages=[{"role": "user", "content": params['text']}],
-        temperature=0,
+
+    response = requests.post(
+        url="https://openrouter.ai/api/v1/chat/completions",
+        headers={
+            "HTTP-Referer": 'https://benchmarks.llmonitor.com', # To identify your app. Can be set to localhost for testing
+            "Authorization": "Bearer " + OPEN_ROUTER_API_KEY
+        },
+        data=json.dumps({
+            "model": model['api_id'],
+            "temperature": 0,
+            "max_tokens": MAX_TOKENS,
+            "stop": [params['stop']] if params.get('stop') else [],
+            "messages": [ 
+                {"role": "user", "content": params['text']}
+            ]
+        })
+    )
+
+    completion = response.json()
+
+    return completion["choices"][0]["message"]["content"]
+
+def anthropic_func(model,params):
+    anthropic = Anthropic(
+        api_key=ANTHROPIC_API_KEY
+    )
+    completion = anthropic.completions.create(
         model=model['api_id'],
-        max_tokens=MAX_TOKENS,
-        headers={"HTTP-Referer": "https://benchmarks.llmonitor.com"},
-        stop=[params['stop']] if params.get('stop') else []
+        temperature=0,
+        max_tokens_to_sample=MAX_TOKENS,
+        prompt=f"{HUMAN_PROMPT} {params['text']}{AI_PROMPT}",
     )
-    
-    return completion.choices[0].message.content
+    return completion.completion
 
 def alephalpha(model, params):
     options = {

run/requirements.txt

@@ -1,6 +1,4 @@
 openai
-pandas
 requests
 python-dotenv
-gradio
 llmonitor

run/run.py

@@ -1,53 +1,68 @@
 import sqlite3
 import time
 from termcolor import colored
-from llmonitor import agent
-from queriers import together, cohere, openai_func, openrouter, ai21, alephalpha
+import psycopg2
+from queriers import together, cohere, openai_func, openrouter, ai21, alephalpha, hugchat_func, anthropic_func
+import psycopg2.extras
+import psycopg2.pool 
 
-db = sqlite3.connect("./database.db")
-db.row_factory = sqlite3.Row
+import os
+from dotenv import load_dotenv
+load_dotenv()
 
-cursor = db.cursor()
+# Connect to database
+PG_URI = os.environ.get("POSTGRES_URL")
+
+
+# Create a connection pool with a minimum of 2 connections and 
+#a maximum of 3 connections 
+pool = psycopg2.pool.SimpleConnectionPool(2, 10, dsn=PG_URI)
+
+#conn = psycopg2.connect(PG_URI)
+
+conn = pool.getconn()
+
+cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
 
 def remove_end(s, suffix):
     if s.endswith(suffix):
         return s[:-len(suffix)]
     return s
 
-
 # Fetch models
-models = cursor.execute("SELECT * FROM models").fetchall()
-models = [dict(model) for model in models]
+cursor.execute("SELECT * FROM models")
+models = cursor.fetchall()
 
 # Fetch prompts
-prompts = cursor.execute("SELECT * FROM prompts").fetchall()
-prompts = [dict(prompt) for prompt in prompts]
+cursor.execute("SELECT * FROM prompts WHERE selected = true")
+prompts = cursor.fetchall()
 
 
 def get_results():
-    results = cursor.execute("SELECT * FROM results").fetchall()
-    print(results[0].keys())
-    return [dict(result) for result in results]
+    cursor.execute("SELECT * FROM results")
+    results = cursor.fetchall()
+    return results
 
 def insert_result(modelId, promptId, result, duration, rate):
     cursor.execute(
-        "INSERT INTO results (model, prompt, result, duration, rate) VALUES (?, ?, ?, ?, ?)",
+        "INSERT INTO results (model, prompt, result, duration, rate) VALUES (%s, %s, %s, %s, %s)",
         (modelId, promptId, result, duration, rate)
     )
-    db.commit()
+    conn.commit()
     pass
 
 def check_if_results_exist(modelId, promptId):
-    results = cursor.execute(
-        "SELECT * FROM results WHERE model = ? AND prompt = ? LIMIT 1", (modelId, promptId)
-    ).fetchall()
+    cursor.execute(
+        "SELECT * FROM results WHERE model = %s AND prompt = %s LIMIT 1", (modelId, promptId)
+    )
+    results = cursor.fetchall()
     return len(results) > 0
 
 def ask_prompt(prompt, model):
     exists = check_if_results_exist(model["id"], prompt["id"])
 
     if exists:
-        print("Skipping, already got benchmark")
+        print(f"Skipping {model['name']}, already got benchmark")
         return
 
     mapping = {
@@ -56,6 +71,8 @@ def ask_prompt(prompt, model):
         "openai": openai_func,
         "openrouter": openrouter,
         "ai21": ai21,
+        "hugchat": hugchat_func,
+        "anthropic": anthropic_func,
         # "alephalpha": alephalpha # TODO: get a working API key
     }
 
@@ -65,7 +82,10 @@ def ask_prompt(prompt, model):
         print(f"No querier for {model['api']}")
         return
 
-    print(f"Querying {model['name']}")
+    print(colored("------------------------------------", 'white'))
+    print(colored(f"Querying {model['name']}", 'white'))
+    print(colored(f"Prompt: {prompt['text']}", 'white'))
+    print(colored("------------------------------------", 'white'))
 
     start_time = time.time()
 
@@ -82,40 +102,45 @@ def ask_prompt(prompt, model):
         duration = end_time - start_time
         chars_per_second = round(len(response_text) / duration, 2)
 
-        print("------------------------------------")
-        print(f"Result: {cleaned}")
-        print(f"Took {duration*1000} ms ({chars_per_second} chars/s)")
-        print("------------------------------------")
+        print(colored("------------------------------------", 'green'))
+        print(colored(f"Result: {cleaned}", 'green'))
+        print(colored(f"Took {duration*1000} ms ({chars_per_second} chars/s)", 'green'))
+        print(colored("------------------------------------", 'green'))
 
         insert_result(model["id"], prompt["id"], cleaned, duration*1000, chars_per_second)
 
     except Exception as e:
-        print(f"Error querying {model['name']}", e)
+        print(colored(f"Error querying {model['name']} ", 'red'), e)
 
 
 total_benchmarks = len(models) * len(prompts)
-print(f"Running {total_benchmarks} benchmarks")
 
-# # Run prompts
-# for model in models:
-#     if model["type"] == "language":
-#         continue
-#     for prompt in prompts:
-#         if prompt["type"] != "code" and model["type"] == "code":
-#             print("Skipping non-code benchmark for code model")
-#             continue
+print(colored(f"Running {total_benchmarks} benchmarks", 'blue'))
 
-#         ask_prompt(prompt, model)
+# Run prompts
+for model in models:
+
+    if model["type"] != "chat":
+        # Skip non-chat models for now 
+        continue
+
+    for prompt in prompts:
+        # if prompt["type"] != "code" and model["type"] == "code":
+            # print("Skipping non-code benchmark for code model")
+            # continue
+
+        ask_prompt(prompt, model)
 
 # Calculate scores
 results = get_results()
 
-@agent(name="RateResult")
+#@agent(name="RateResult")
 def rate_result(result):
-    rubrics = cursor.execute(
-        "SELECT * FROM rubrics WHERE prompt = ?",
+    cursor.execute(
+        "SELECT * FROM rubrics WHERE prompt = %s",
         (result["prompt"],)
-    ).fetchall()
+    )
+    rubrics = cursor.fetchall()
 
     has_rubrics = len(rubrics) > 0
 
@@ -128,7 +153,7 @@ def rate_result(result):
     print(colored(result["result"], 'cyan'))
     print(colored('---------------------------', 'white'))
     
-    score = None 
+    score = 0 
 
     for rubric in rubrics:
 
@@ -138,10 +163,11 @@ def rate_result(result):
             score = 0
         else:
             grading_text = (
-                f'You help verify that the following answer match this condition: the answer {rubric["grading"]}. Note: the answer might be imcomplete, in which case do your best to assess based on what the full result would be.\n\n'
+                f'You help me grade the answer of chatbots by verifying that they match this condition: the answer {rubric["grading"]}. Note: the answer might be imcomplete, in which case do your best to assess based on what the full result would be. Your rating needs to be very strict: if I ask that the answer is *exactly* some string and it contains more than that, then it\'s invalid.\n\n'
                 f'\n\n--START OF THE ANSWER--\n{result["result"]}\n--END OF THE ANSWER--\n\n'
-                f'Take a deep breath and explain step by step how you come to the conclusion.'
-                f'Finally, reply on the last line with YES if the following answer matches this condition (otherwies reply NO).'
+                # f'Take a deep breath and explain step by step how you come to the conclusion.'
+                # f'Finally, reply on the last line with YES if the following answer matches this condition (otherwies reply NO).'
+                f'Reply with YES if the text between START and END matches exactly the above condition (otherwise reply NO).'
             )
 
             # get gpt-4 model
@@ -164,17 +190,16 @@ def rate_result(result):
     
     return score
 
-
-
 for result in results:
-    if not result["score"]:
+    if result["score"] is None:
         score = rate_result(result)
 
         if score is not None:
             cursor.execute(
-                "UPDATE results SET score = ? WHERE id == ?",
+                "UPDATE results SET score = %s WHERE id = %s",
                 (score, result["id"])
             )
-            db.commit()
+            conn.commit()
 
-db.close() 
+cursor.close()
+conn.close()

run/test.py

@@ -0,0 +1,19 @@
+import requests
+import json
+
+response = requests.post(
+  url="https://openrouter.ai/api/v1/chat/completions",
+  headers={
+    "HTTP-Referer": 'https://benchmarks.llmonitor.com', # To identify your app. Can be set to localhost for testing
+    "Authorization": "Bearer " + "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"
+  },
+  data=json.dumps({
+    "model": "mistralai/mistral-7b-instruct", # Optional
+    "messages": [ 
+      {"role": "user", "content": "What is the meaning of life?"}
+    ]
+  })
+)
+
+
+print(response.json())

styles/globals.css

@@ -14,6 +14,9 @@ a {
   /* text-decoration: none; */
 }
 
+p {
+  margin-bottom: 10px;
+}
 
 
 ul {
@@ -24,6 +27,15 @@ li {
   margin-bottom: 10px;
 }
 
+pre {
+  background-color: #eee;
+  padding: 10px;
+  border-radius: 5px;
+  white-space: pre-wrap; 
+  word-wrap: break-word;
+}
+
+
 table {
   /* border-collapse: collapse; */
   width: 100%;
@@ -39,7 +51,7 @@ th, td {
   text-align: left;
 }
 
-th:nth-child(1) {
+/* th:nth-child(1) {
   width: 200px;
 }
 
@@ -47,18 +59,13 @@ th:nth-child(2) {
   width: auto;
 }
 
-pre {
-  background-color: #eee;
-  padding: 10px;
-  border-radius: 5px;
-  white-space: pre-wrap; 
-  word-wrap: break-word;
-}
-
 th:nth-child(3),
 th:nth-child(4) {
   width: 100px;
-}
+} */
+
+
+
 
 @media (max-width: 800px) {
   table {
@@ -68,7 +75,7 @@ th:nth-child(4) {
     max-width: 700px;
   }
 
-  th:nth-child(1) {
+  /* th:nth-child(1) {
    width: 50px;
   }
 
@@ -78,7 +85,7 @@ th:nth-child(4) {
   th:nth-child(4),
   td:nth-child(4) {
     display: none;
-  }
+  } */
 }
 
 @media (prefers-color-scheme: dark) {
@@ -112,4 +119,13 @@ th:nth-child(4) {
 
 .llmonitor:hover {
   transform: scale(1.1);
+}
+
+input, textarea {
+  font-size: 10px;
+  font-family: monospace;
+}
+
+button {
+  padding: 5px 10px;
 }

utils/db.js

@@ -0,0 +1,21 @@
+import postgres from "postgres"
+import { cache } from "react"
+
+const sql = postgres(process.env.POSTGRES_URL) // will use psql environment variables
+
+export const getModels = cache(async () => {
+  const models = await sql`
+    SELECT models.*, SUM(results.score) as total_score
+    FROM models
+    LEFT JOIN results ON models.id = results.model
+    GROUP BY models.id
+    ORDER BY total_score DESC;
+  `
+
+  return models.map((m) => ({
+    ...m,
+    slug: m.api_id.split("/").pop().toLowerCase(),
+  }))
+})
+
+export default sql

utils/email.js

@@ -0,0 +1,16 @@
+export const sendEmail = async (body) => {
+  if (!process.env.RESEND_KEY) {
+    return console.warn("RESEND_KEY is not set, skipping email sending")
+  }
+
+  const res = await fetch("https://api.resend.com/emails", {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${process.env.RESEND_KEY}`,
+    },
+    body: JSON.stringify(body),
+  })
+
+  return await res.json()
+}

utils/getDB.js

@@ -1,13 +0,0 @@
-import sqlite3 from "sqlite3"
-import { open } from "sqlite"
-
-const getDB = async () => {
-  const db = await open({
-    filename: process.cwd() + "/run/database.db",
-    driver: sqlite3.Database,
-  })
-
-  return db
-}
-
-export default getDB