diff --git a/.github/workflows/evals.yml b/.github/workflows/evals.yml new file mode 100644 index 0000000..aab65cf --- /dev/null +++ b/.github/workflows/evals.yml @@ -0,0 +1,49 @@ +name: Skill Evals + +on: + workflow_dispatch: + pull_request: + types: [opened, synchronize, labeled] + paths: + - "skills/**" + - "packages/evals/**" + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +permissions: + pull-requests: write + contents: read + +jobs: + eval: + name: Run evals + if: > + github.event_name == 'workflow_dispatch' || + (github.event_name == 'pull_request' && + contains(github.event.pull_request.labels.*.name, 'run-evals')) + runs-on: ubuntu-latest + environment: evals + timeout-minutes: 30 + + env: + BRAINTRUST_PROJECT_ID: ${{ secrets.BRAINTRUST_PROJECT_ID }} + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + + steps: + - uses: actions/checkout@v4 + + - uses: jdx/mise-action@v3 + with: + install: true + + - name: Install dependencies + run: npm install && npm --prefix packages/evals install + + - name: Run Evals + uses: braintrustdata/eval-action@v1 + with: + api_key: ${{ secrets.BRAINTRUST_API_KEY }} + runtime: node + root: packages/evals diff --git a/AGENTS.md b/AGENTS.md index fc7e90f..f0e915f 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -25,6 +25,16 @@ skills/ packages/ skills-build/ # Generic build system for all skills evals/ # LLM evaluation system for skills + AGENTS.md # Agent guide for developing evals + CLAUDE.md # Symlink to AGENTS.md + scenarios/ + workflow-scenarios.json # Handwritten workflow test scenarios + src/ + cli.ts # Entry point + prompts/ # Eval and judge prompts + scorer/ # Zod schemas and judge execution + dataset/ # Test case extraction from skill references + runner/ # Eval orchestrator and runners ``` ## Commands diff --git a/mise.toml b/mise.toml index 97113a7..9b59f3a 100644 --- a/mise.toml +++ b/mise.toml @@ -46,16 +46,11 @@ sources = ["test/**", "skills/**"] # ── Eval tasks ──────────────────────────────────────────────────────── [tasks.eval] -description = "Run all evals" -run = "tsx packages/evals/src/cli.ts" +description = "Run code-fix evals (local, no upload)" +run = "npm --prefix packages/evals run eval" sources = ["packages/evals/src/**", "skills/**/references/**"] -[tasks."eval:code-fix"] -description = "Run code-fix evals" -run = "tsx packages/evals/src/cli.ts --type code-fix" -sources = ["packages/evals/src/**", "skills/**/references/**"] - -[tasks."eval:workflow"] -description = "Run workflow evals" -run = "tsx packages/evals/src/cli.ts --type workflow" +[tasks."eval:upload"] +description = "Run code-fix evals and upload to Braintrust" +run = "npm --prefix packages/evals run eval:upload" sources = ["packages/evals/src/**", "skills/**/references/**"] diff --git a/packages/evals/.env.example b/packages/evals/.env.example new file mode 100644 index 0000000..f31f947 --- /dev/null +++ b/packages/evals/.env.example @@ -0,0 +1,3 @@ +ANTHROPIC_API_KEY= +BRAINTRUST_API_KEY= +BRAINTRUST_PROJECT_ID= diff --git a/packages/evals/AGENTS.md b/packages/evals/AGENTS.md new file mode 100644 index 0000000..aa58ae5 --- /dev/null +++ b/packages/evals/AGENTS.md @@ -0,0 +1,142 @@ +# Evals — Agent Guide + +This package evaluates whether LLMs correctly apply Supabase best practices +using skill documentation as context. It uses +[Braintrust](https://www.braintrust.dev/) for eval orchestration and the +[Vercel AI SDK](https://sdk.vercel.ai/) for LLM calls. + +## Architecture + +Two-step **LLM-as-judge** pattern powered by Braintrust's `Eval()`: + +1. The **eval model** (default: `claude-sonnet-4-5-20250929`) receives a prompt + with skill context and produces a code fix. +2. Three independent **judge scorers** (default: `claude-opus-4-6`) evaluate the + fix via structured output (Zod schemas via AI SDK's `Output.object()`). + +Key files: + +``` +src/ + code-fix.eval.ts # Braintrust Eval() entry point + dataset.ts # Maps extracted test cases to EvalCase format + scorer.ts # Three AI SDK-based scorers (Correctness, Completeness, Best Practice) + models.ts # Model provider factory (Anthropic / OpenAI) + dataset/ + types.ts # CodeFixTestCase interface + extract.ts # Auto-extracts test cases from skill references + prompts/ + code-fix.ts # System + user prompts for the eval model +``` + +## How It Works + +**Test cases are auto-extracted** from `skills/*/references/*.md`. The extractor +(`dataset/extract.ts`) finds consecutive `**Incorrect:**` / `**Correct:**` code +block pairs under `##` sections. Each pair becomes one test case. + +Three independent scorers evaluate each fix (0–1 scale): + +- **Correctness** — does the fix address the core issue? +- **Completeness** — does the fix include all necessary changes? +- **Best Practice** — does the fix follow Supabase conventions? + +Braintrust aggregates the scores and provides a dashboard for tracking +regressions over time. + +## Adding Test Cases + +No code changes needed. Add paired Incorrect/Correct blocks to any skill +reference file. The extractor picks them up automatically. + +Required format in a reference `.md` file: + +```markdown +## Section Title + +Explanation of the issue. + +**Incorrect:** + +\```sql +-- bad code +\``` + +**Correct:** + +\```sql +-- good code +\``` +``` + +Rules: + +- Pairs must be consecutive — an Incorrect block immediately followed by a + Correct block +- Labels are matched case-insensitively. Bad labels: `Incorrect`, `Wrong`, `Bad`. + Good labels: `Correct`, `Good`, `Usage`, `Implementation`, `Example`, + `Recommended` +- The optional parenthetical in the label becomes the `description` field: + `**Incorrect (missing RLS):**` +- Files prefixed with `_` (like `_sections.md`, `_template.md`) are skipped +- Each pair gets an ID like `supabase/db-rls-mandatory#0` (skill/filename#index) + +## Modifying Prompts + +- `src/prompts/code-fix.ts` — what the eval model sees +- `src/scorer.ts` — judge prompts for each scorer dimension + +Temperature settings: + +- Eval model: `0.2` (in `code-fix.eval.ts`) +- Judge model: `0.1` (in `scorer.ts`) + +## Modifying Scoring + +Each scorer in `src/scorer.ts` is independent. To add a new dimension: + +1. Create a new `EvalScorer` function in `scorer.ts` +2. Add it to the `scores` array in `code-fix.eval.ts` + +## Running Evals + +```bash +# Run locally (no Braintrust upload) +mise run eval + +# Run and upload to Braintrust dashboard +mise run eval:upload +``` + +Or directly: + +```bash +cd packages/evals + +# Local run +npx braintrust eval --no-send-logs src/code-fix.eval.ts + +# Upload to Braintrust +npx braintrust eval src/code-fix.eval.ts +``` + +In CI, evals run via `braintrustdata/eval-action@v1` and are gated by the +`run-evals` PR label. + +## Environment + +API keys are loaded by mise from `packages/evals/.env` (configured in root +`mise.toml`). Copy `.env.example` to `.env` and fill in the keys. + +``` +ANTHROPIC_API_KEY=sk-ant-... # Required: eval model + judge model +BRAINTRUST_API_KEY=... # Required for upload to Braintrust dashboard +BRAINTRUST_PROJECT_ID=... # Required for upload to Braintrust dashboard +``` + +Optional overrides: + +``` +EVAL_MODEL=claude-sonnet-4-5-20250929 # Model under test +EVAL_JUDGE_MODEL=claude-opus-4-6 # Judge model for scorers +``` diff --git a/packages/evals/CLAUDE.md b/packages/evals/CLAUDE.md new file mode 120000 index 0000000..47dc3e3 --- /dev/null +++ b/packages/evals/CLAUDE.md @@ -0,0 +1 @@ +AGENTS.md \ No newline at end of file diff --git a/packages/evals/README.md b/packages/evals/README.md new file mode 100644 index 0000000..250f6e6 --- /dev/null +++ b/packages/evals/README.md @@ -0,0 +1,46 @@ +# Evals + +LLM evaluation system for Supabase agent skills, powered by [Braintrust](https://www.braintrust.dev/). Tests whether models can correctly apply Supabase best practices using skill documentation as context. + +## How It Works + +Each eval follows a two-step **LLM-as-judge** pattern orchestrated by Braintrust's `Eval()`: + +1. **Generate** — The eval model (e.g. Sonnet 4.5) receives a prompt with skill context and produces a code fix. +2. **Judge** — Three independent scorers using a stronger model (Opus 4.6 by default) evaluate the fix via the Vercel AI SDK with structured output. + +Test cases are extracted automatically from skill reference files (`skills/*/references/*.md`). Each file contains paired **Incorrect** / **Correct** code blocks — the model receives the bad code and must produce the fix. + +**Scoring dimensions (each 0–1):** + +| Scorer | Description | +|--------|-------------| +| Correctness | Does the fix address the core issue? | +| Completeness | Does it include all necessary changes? | +| Best Practice | Does it follow Supabase best practices? | + +## Usage + +```bash +# Run locally (no Braintrust upload) +mise run eval + +# Run and upload to Braintrust dashboard +mise run eval:upload +``` + +### Environment Variables + +API keys are loaded via mise from `packages/evals/.env` (see root `mise.toml`). + +``` +ANTHROPIC_API_KEY Required: eval model + judge model +BRAINTRUST_API_KEY Required for Braintrust dashboard upload +BRAINTRUST_PROJECT_ID Required for Braintrust dashboard upload +EVAL_MODEL Override default eval model (claude-sonnet-4-5-20250929) +EVAL_JUDGE_MODEL Override default judge model (claude-opus-4-6) +``` + +## Adding Test Cases + +Add paired Incorrect/Correct code blocks to any skill reference file. 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"@ai-sdk/openai": "^3.0.29", + "ai": "^6.0.86", + "braintrust": "^1.0.2", + "zod": "^3.23.0" + }, + "devDependencies": { + "@types/node": "^20.10.0", + "tsx": "^4.7.0", + "typescript": "^5.3.0" + } +} diff --git a/packages/evals/src/code-fix.eval.ts b/packages/evals/src/code-fix.eval.ts new file mode 100644 index 0000000..3188837 --- /dev/null +++ b/packages/evals/src/code-fix.eval.ts @@ -0,0 +1,36 @@ +import assert from "node:assert"; +import { generateText } from "ai"; +import { Eval } from "braintrust"; +import { dataset } from "./dataset.js"; +import { getModel } from "./models.js"; +import { + buildCodeFixPrompt, + buildCodeFixSystemPrompt, +} from "./prompts/code-fix.js"; +import { + bestPracticeScorer, + completenessScorer, + correctnessScorer, +} from "./scorer.js"; + +assert(process.env.ANTHROPIC_API_KEY, "ANTHROPIC_API_KEY is not set"); + +const modelId = process.env.EVAL_MODEL || "claude-sonnet-4-5-20250929"; + +Eval("CodeFix", { + projectId: process.env.BRAINTRUST_PROJECT_ID, + trialCount: process.env.CI ? 3 : 1, + data: () => dataset(), + task: async (input) => { + const model = getModel(modelId); + const response = await generateText({ + model, + system: buildCodeFixSystemPrompt(), + prompt: buildCodeFixPrompt(input.testCase), + temperature: 0.2, + maxRetries: 2, + }); + return { llmOutput: response.text }; + }, + scores: [correctnessScorer, completenessScorer, bestPracticeScorer], +}); diff --git a/packages/evals/src/dataset.ts b/packages/evals/src/dataset.ts new file mode 100644 index 0000000..80990b5 --- /dev/null +++ b/packages/evals/src/dataset.ts @@ -0,0 +1,33 @@ +import type { EvalCase } from "braintrust"; +import { extractCodeFixDataset } from "./dataset/extract.js"; +import type { CodeFixTestCase } from "./dataset/types.js"; + +export type Input = { testCase: CodeFixTestCase }; + +export type Expected = { + correctCode: string; + correctLanguage?: string; +}; + +export type Metadata = { + skillName: string; + section: string; + tags: string[]; +}; + +export type Output = { llmOutput: string }; + +export function dataset(): EvalCase[] { + return extractCodeFixDataset().map((tc) => ({ + input: { testCase: tc }, + expected: { + correctCode: tc.goodExample.code, + correctLanguage: tc.goodExample.language, + }, + metadata: { + skillName: tc.skillName, + section: tc.section, + tags: tc.tags, + }, + })); +} diff --git a/packages/evals/src/dataset/extract.ts b/packages/evals/src/dataset/extract.ts new file mode 100644 index 0000000..ea66859 --- /dev/null +++ b/packages/evals/src/dataset/extract.ts @@ -0,0 +1,277 @@ +import { existsSync, readdirSync, readFileSync } from "node:fs"; +import { basename, join, resolve } from "node:path"; +import type { CodeFixTestCase } from "./types.js"; + +function findSkillsRoot(): string { + let dir = process.cwd(); + for (let i = 0; i < 10; i++) { + const candidate = join(dir, "skills"); + if (existsSync(candidate)) return candidate; + const parent = resolve(dir, ".."); + if (parent === dir) break; + dir = parent; + } + throw new Error( + "Could not find skills/ directory. Run from the repository root or a subdirectory.", + ); +} + +const SKILLS_ROOT = findSkillsRoot(); + +// --- Duplicated from skills-build/src/parser.ts for isolation --- + +interface CodeExample { + label: string; + description?: string; + code: string; + language?: string; +} + +function parseFrontmatter(content: string): { + frontmatter: Record; + body: string; +} { + const frontmatter: Record = {}; + + if (!content.startsWith("---")) { + return { frontmatter, body: content }; + } + + const endIndex = content.indexOf("---", 3); + if (endIndex === -1) { + return { frontmatter, body: content }; + } + + const frontmatterContent = content.slice(3, endIndex).trim(); + const body = content.slice(endIndex + 3).trim(); + + for (const line of frontmatterContent.split("\n")) { + const colonIndex = line.indexOf(":"); + if (colonIndex === -1) continue; + + const key = line.slice(0, colonIndex).trim(); + let value = line.slice(colonIndex + 1).trim(); + + if ( + (value.startsWith('"') && value.endsWith('"')) || + (value.startsWith("'") && value.endsWith("'")) + ) { + value = value.slice(1, -1); + } + + frontmatter[key] = value; + } + + return { frontmatter, body }; +} + +function extractTitle(body: string): string | null { + const match = body.match(/^##\s+(.+)$/m); + return match ? match[1].trim() : null; +} + +interface Section { + title: string; + explanation: string; + examples: CodeExample[]; +} + +function extractSections(body: string): Section[] { + const sections: Section[] = []; + const lines = body.split("\n"); + + let currentTitle = ""; + let explanationLines: string[] = []; + let currentExamples: CodeExample[] = []; + let currentLabel = ""; + let currentDescription = ""; + let inCodeBlock = false; + let codeBlockLang = ""; + let codeBlockContent: string[] = []; + let collectingExplanation = false; + + function flushExample() { + if (currentLabel && codeBlockContent.length > 0) { + currentExamples.push({ + label: currentLabel, + description: currentDescription || undefined, + code: codeBlockContent.join("\n"), + language: codeBlockLang || undefined, + }); + } + currentLabel = ""; + currentDescription = ""; + codeBlockContent = []; + codeBlockLang = ""; + } + + function flushSection() { + if (currentTitle && currentExamples.length > 0) { + sections.push({ + title: currentTitle, + explanation: explanationLines.join("\n").trim(), + examples: currentExamples, + }); + } + currentExamples = []; + explanationLines = []; + } + + for (const line of lines) { + if (line.startsWith("## ") && !inCodeBlock) { + flushExample(); + flushSection(); + currentTitle = line.replace(/^##\s+/, "").trim(); + collectingExplanation = true; + continue; + } + + const labelMatch = line.match( + /^\*\*([^*]+?)(?:\s*\(([^)]+)\))?\s*:\*\*\s*$/, + ); + if (labelMatch && !inCodeBlock) { + collectingExplanation = false; + flushExample(); + currentLabel = labelMatch[1].trim(); + currentDescription = labelMatch[2]?.trim() || ""; + continue; + } + + if (line.startsWith("```") && !inCodeBlock) { + collectingExplanation = false; + inCodeBlock = true; + codeBlockLang = line.slice(3).trim(); + continue; + } + + if (line.startsWith("```") && inCodeBlock) { + inCodeBlock = false; + continue; + } + + if (inCodeBlock) { + codeBlockContent.push(line); + } else if (collectingExplanation) { + explanationLines.push(line); + } + } + + flushExample(); + flushSection(); + + return sections; +} + +// --- Duplicated from skills-build/src/validate.ts --- + +function isBadExample(label: string): boolean { + const lower = label.toLowerCase(); + return ( + lower.includes("incorrect") || + lower.includes("wrong") || + lower.includes("bad") + ); +} + +function isGoodExample(label: string): boolean { + const lower = label.toLowerCase(); + return ( + lower.includes("correct") || + lower.includes("good") || + lower.includes("usage") || + lower.includes("implementation") || + lower.includes("example") || + lower.includes("recommended") + ); +} + +// --- Extraction logic --- + +function pairExamples( + examples: CodeExample[], +): Array<{ bad: CodeExample; good: CodeExample }> { + const pairs: Array<{ bad: CodeExample; good: CodeExample }> = []; + + for (let i = 0; i < examples.length - 1; i++) { + if ( + isBadExample(examples[i].label) && + isGoodExample(examples[i + 1].label) + ) { + pairs.push({ bad: examples[i], good: examples[i + 1] }); + } + } + + return pairs; +} + +function discoverSkillNames(): string[] { + if (!existsSync(SKILLS_ROOT)) return []; + + return readdirSync(SKILLS_ROOT, { withFileTypes: true }) + .filter((d) => d.isDirectory()) + .filter((d) => existsSync(join(SKILLS_ROOT, d.name, "SKILL.md"))) + .map((d) => d.name); +} + +function getMarkdownFiles(dir: string): string[] { + if (!existsSync(dir)) return []; + + return readdirSync(dir) + .filter((f) => f.endsWith(".md") && !f.startsWith("_")) + .map((f) => join(dir, f)); +} + +export function extractCodeFixDataset(skillName?: string): CodeFixTestCase[] { + const skills = skillName ? [skillName] : discoverSkillNames(); + const testCases: CodeFixTestCase[] = []; + + for (const skill of skills) { + const referencesDir = join(SKILLS_ROOT, skill, "references"); + const files = getMarkdownFiles(referencesDir); + + for (const filePath of files) { + const content = readFileSync(filePath, "utf-8"); + const { frontmatter, body } = parseFrontmatter(content); + const fileTitle = + frontmatter.title || extractTitle(body) || basename(filePath, ".md"); + const tags = frontmatter.tags?.split(",").map((t) => t.trim()) || []; + const section = basename(filePath, ".md").split("-")[0]; + + const sections = extractSections(body); + let pairIndex = 0; + + for (const sec of sections) { + const pairs = pairExamples(sec.examples); + + for (const { bad, good } of pairs) { + testCases.push({ + id: `${skill}/${basename(filePath, ".md")}#${pairIndex}`, + skillName: skill, + referenceFile: filePath, + referenceFilename: basename(filePath), + title: sec.title || fileTitle, + explanation: sec.explanation, + section, + tags, + pairIndex, + badExample: { + label: bad.label, + description: bad.description, + code: bad.code, + language: bad.language, + }, + goodExample: { + label: good.label, + description: good.description, + code: good.code, + language: good.language, + }, + }); + pairIndex++; + } + } + } + } + + return testCases; +} diff --git a/packages/evals/src/dataset/types.ts b/packages/evals/src/dataset/types.ts new file mode 100644 index 0000000..830da32 --- /dev/null +++ b/packages/evals/src/dataset/types.ts @@ -0,0 +1,24 @@ +export interface CodeFixTestCase { + /** Unique ID, e.g. "supabase/db-rls-mandatory#0" */ + id: string; + skillName: string; + referenceFile: string; + referenceFilename: string; + title: string; + explanation: string; + section: string; + tags: string[]; + pairIndex: number; + badExample: { + label: string; + description?: string; + code: string; + language?: string; + }; + goodExample: { + label: string; + description?: string; + code: string; + language?: string; + }; +} diff --git a/packages/evals/src/models.ts b/packages/evals/src/models.ts new file mode 100644 index 0000000..b4dcf4c --- /dev/null +++ b/packages/evals/src/models.ts @@ -0,0 +1,51 @@ +import type { AnthropicProvider } from "@ai-sdk/anthropic"; +import { anthropic } from "@ai-sdk/anthropic"; +import type { OpenAIProvider } from "@ai-sdk/openai"; +import { openai } from "@ai-sdk/openai"; +import type { LanguageModel } from "ai"; + +/** Model ID accepted by the Anthropic provider (string literal union + string). */ +export type AnthropicModelId = Parameters[0]; + +/** Model ID accepted by the OpenAI provider (string literal union + string). */ +export type OpenAIModelId = Parameters[0]; + +/** Any model ID accepted by the eval harness. */ +export type SupportedModelId = AnthropicModelId | OpenAIModelId; + +const MODEL_MAP: Record LanguageModel> = { + "claude-opus-4-6": () => anthropic("claude-opus-4-6"), + "claude-sonnet-4-5-20250929": () => anthropic("claude-sonnet-4-5-20250929"), + "claude-haiku-4-5-20251001": () => anthropic("claude-haiku-4-5-20251001"), + "claude-opus-4-5-20251101": () => anthropic("claude-opus-4-5-20251101"), + "claude-sonnet-4-20250514": () => anthropic("claude-sonnet-4-20250514"), + "gpt-4o": () => openai("gpt-4o"), + "gpt-4o-mini": () => openai("gpt-4o-mini"), + "o3-mini": () => openai("o3-mini"), +}; + +export function getModel(modelId: SupportedModelId): LanguageModel { + const factory = MODEL_MAP[modelId]; + if (factory) return factory(); + + // Fall back to provider detection from model ID prefix + if (modelId.startsWith("claude")) { + return anthropic(modelId as AnthropicModelId); + } + if ( + modelId.startsWith("gpt") || + modelId.startsWith("o1") || + modelId.startsWith("o3") + ) { + return openai(modelId as OpenAIModelId); + } + + throw new Error( + `Unknown model: ${modelId}. Available: ${Object.keys(MODEL_MAP).join(", ")}`, + ); +} + +export function getJudgeModel(): LanguageModel { + const judgeModelId = process.env.EVAL_JUDGE_MODEL || "claude-opus-4-6"; + return getModel(judgeModelId); +} diff --git a/packages/evals/src/prompts/code-fix.ts b/packages/evals/src/prompts/code-fix.ts new file mode 100644 index 0000000..993c797 --- /dev/null +++ b/packages/evals/src/prompts/code-fix.ts @@ -0,0 +1,33 @@ +import type { CodeFixTestCase } from "../dataset/types.js"; + +export function buildCodeFixSystemPrompt(): string { + return `You are a senior Supabase developer and database architect. You fix code to follow Supabase best practices including: +- Row Level Security (RLS) policies +- Proper authentication patterns +- Safe migration workflows +- Correct SDK usage patterns +- Edge Function best practices +- Connection pooling configuration +- Security-first defaults + +When fixing code, ensure the fix is complete, production-ready, and follows the latest Supabase conventions. Return only the corrected code inside a single code block.`; +} + +export function buildCodeFixPrompt(testCase: CodeFixTestCase): string { + const langHint = testCase.badExample.language + ? ` (${testCase.badExample.language})` + : ""; + + return `The following code has a problem related to: ${testCase.title} + +Context: ${testCase.explanation} + +Here is the problematic code${langHint}: + +\`\`\`${testCase.badExample.language || ""} +${testCase.badExample.code} +\`\`\` +${testCase.badExample.description ? `\nIssue hint: ${testCase.badExample.description}` : ""} + +Fix this code to follow Supabase best practices. Return ONLY the corrected code inside a single code block. Do not include any explanation outside the code block.`; +} diff --git a/packages/evals/src/scorer.ts b/packages/evals/src/scorer.ts new file mode 100644 index 0000000..e3d50ff --- /dev/null +++ b/packages/evals/src/scorer.ts @@ -0,0 +1,126 @@ +import { generateText, Output } from "ai"; +import type { EvalScorer } from "braintrust"; +import { z } from "zod"; +import type { CodeFixTestCase } from "./dataset/types.js"; +import type { Expected, Input, Output as TaskOutput } from "./dataset.js"; +import { getModel } from "./models.js"; + +const judgeModelId = process.env.EVAL_JUDGE_MODEL || "claude-opus-4-6"; + +const scoreSchema = z.object({ + score: z + .number() + .describe("Score from 0 to 1 (0 = bad, 0.5 = partial, 1 = good)"), + reasoning: z.string().describe("Brief reasoning for the score"), +}); + +const SYSTEM_PROMPT = + "You are a precise, consistent evaluator of Supabase code fixes. You assess whether LLM-generated code correctly addresses Supabase anti-patterns by comparing against reference solutions. You are fair: functionally equivalent solutions that differ in style or approach from the reference still receive high scores. You are strict: partial fixes, missing security measures, or incorrect patterns receive low scores. Always provide specific evidence for your scoring."; + +function buildContext(tc: CodeFixTestCase, llmOutput: string): string { + return `## Reference Information + +**Topic:** ${tc.title} +**Explanation:** ${tc.explanation} + +## Original Incorrect Code + +\`\`\`${tc.badExample.language || ""} +${tc.badExample.code} +\`\`\` + +## Reference Correct Code (ground truth) + +\`\`\`${tc.goodExample.language || ""} +${tc.goodExample.code} +\`\`\` + +## LLM's Attempted Fix + +${llmOutput}`; +} + +async function judge( + prompt: string, +): Promise<{ score: number; reasoning: string }> { + const model = getModel(judgeModelId); + const { output } = await generateText({ + model, + system: SYSTEM_PROMPT, + prompt, + output: Output.object({ schema: scoreSchema }), + temperature: 0.1, + maxRetries: 2, + }); + if (!output) throw new Error("Judge returned no structured output"); + return output; +} + +export const correctnessScorer: EvalScorer< + Input, + TaskOutput, + Expected +> = async ({ input, output }) => { + const context = buildContext(input.testCase, output.llmOutput); + const result = await judge(`${context} + +## Task + +Evaluate **correctness**: Does the LLM's fix address the core issue identified in the incorrect code? + +The fix does not need to be character-identical to the reference, but it must solve the same problem. Functionally equivalent or improved solutions should score well. + +Score 1 if the fix fully addresses the core issue, 0.5 if it partially addresses it, 0 if it fails to address the core issue or introduces new problems.`); + + return { + name: "Correctness", + score: result.score, + metadata: { reasoning: result.reasoning }, + }; +}; + +export const completenessScorer: EvalScorer< + Input, + TaskOutput, + Expected +> = async ({ input, output }) => { + const context = buildContext(input.testCase, output.llmOutput); + const result = await judge(`${context} + +## Task + +Evaluate **completeness**: Does the LLM's fix include ALL necessary changes shown in the reference? + +Check for missing RLS enablement, missing policy clauses, missing columns, incomplete migrations, or any partial fixes. The fix should be production-ready. + +Score 1 if all necessary changes are present, 0.5 if most changes are present but some are missing, 0 if significant changes are missing.`); + + return { + name: "Completeness", + score: result.score, + metadata: { reasoning: result.reasoning }, + }; +}; + +export const bestPracticeScorer: EvalScorer< + Input, + TaskOutput, + Expected +> = async ({ input, output }) => { + const context = buildContext(input.testCase, output.llmOutput); + const result = await judge(`${context} + +## Task + +Evaluate **best practices**: Does the LLM's fix follow Supabase best practices as demonstrated in the reference? + +Consider: RLS patterns, auth.users references, migration conventions, connection pooling, edge function patterns, SDK usage, and security-first defaults. Alternative correct approaches that achieve the same security/correctness goal are acceptable. + +Score 1 if the fix follows best practices, 0.5 if it mostly follows best practices with minor deviations, 0 if it uses anti-patterns or ignores conventions.`); + + return { + name: "Best Practice", + score: result.score, + metadata: { reasoning: result.reasoning }, + }; +}; diff --git a/packages/evals/tsconfig.json b/packages/evals/tsconfig.json new file mode 100644 index 0000000..c0524f9 --- /dev/null +++ b/packages/evals/tsconfig.json @@ -0,0 +1,16 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ESNext", + "moduleResolution": "bundler", + "esModuleInterop": true, + "strict": true, + "skipLibCheck": true, + "outDir": "dist", + "rootDir": "src", + "declaration": true, + "resolveJsonModule": true + }, + "include": ["src/**/*"], + "exclude": ["node_modules", "dist"] +}