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Squashed commit of the following:
commit b2b0470a1d9267d042ec0ac66523c6635bf5b199
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 13:13:38 2026 +0200
chore: update .gitignore to include .vite-hooks and modify pnpm-lock.yaml for dependencies
commit d28fadb5cd8706c874e616102878b4a394ec84c1
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 13:08:04 2026 +0200
fix: remove timestamp conflict guard
commit c6998d9dbab19d09d3c8054feef1d2e4117555eb
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 12:11:51 2026 +0200
chore(release): v5.1.5
commit f33d168711804880e1f12e88d24290aae16cc258
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 11:58:35 2026 +0200
revert: compose.yml
commit d961e6535811a10c335525fb33a08d03e737278d
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 11:58:08 2026 +0200
refactor(agent): replace 'revert' terminology with 'restore' for clarity, resolves #3086
commit 17f351171be218e33f01c469d95e4164d4c8dc57
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 11:10:41 2026 +0200
refactor(pdf): simplify sidebar section filtering and update summary feature logic
commit d55179b9d76879e3204de185e8b53fadd0a107ed
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 09:53:37 2026 +0200
chore: update pnpm-lock.yaml and turbo.json
commit 7cade6980e1a04352536bd44ef773f338c4ef599
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 09:38:30 2026 +0200
fix(polyfill): add tested polyfill for Map Upsert methods
commit 26d175bb9c53d93225d1e907678445252c13d660
Merge: 1cf33dc6c 5b1297fa2
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 09:23:29 2026 +0200
Merge remote-tracking branch 'origin/main' into feat/explore-hono-orpc-migration
# Conflicts:
# packages/api/src/services/agent-url.ts
# packages/runtime-externals/package.json
commit 1cf33dc6c9d81735730ad656e16dab6501c6d6a1
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Tue May 19 09:22:12 2026 +0200
chore: preserve branch changes before main sync
commit b380a4b00fdbcdd81ff4f8ef72b330fd027ccda5
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Mon May 18 07:50:28 2026 +0200
chore: lot of fixes for monorepo migration
commit 8fcf0ec64e1c29572ebaff494338368bfcf75760
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 13:57:17 2026 +0200
chore: update knip version and refine web app routing with new SEO endpoints
commit 234e68086ff15610a93877354c98e2c020364533
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 12:10:06 2026 +0200
refactor(auth): update OAuth routes to include API prefix and remove unused schema endpoint
commit 91c84b9a8496b0ce21d71cae9f8b2a027638c9ac
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:54:29 2026 +0200
chore: update dependencies and enhance PWA metadata in web app
commit 150117d4a5a9dd6cd92c64891aad8cae90f6a7af
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:12:35 2026 +0200
docs: revise manifest-only pwa testing scope
commit 6b939a55661aec9dd8122b184e4b60a5c7325fb5
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:11:33 2026 +0200
docs: add manifest-only pwa design
commit 1422e1fc96c400948b273210a1067251087d15d4
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:05:04 2026 +0200
chore(dev): simplify server proxy config
commit bc2ff5a9f6fda41e6c40333c8f163aa23a6c5e48
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:04:50 2026 +0200
docs: add unsafe oauth redirect plan
commit 445359ebe9b96c1515bf1c4c3f73ba8a8448ec12
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 11:04:34 2026 +0200
feat(auth): add unsafe oauth redirect flag
commit 73fffdd24598e56b2793f7657919bc794835892e
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 10:55:02 2026 +0200
docs: design unsafe oauth redirect flag
commit c0066aa19c15fc8a4c8e5179ed49889c117519f4
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 10:22:04 2026 +0200
chore: update translation source paths
commit 9033da082418d252aafd6c2eed72f71f014be3d9
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 10:09:25 2026 +0200
refactor(arch): react spa + hono migration
commit 6f27936c11bda895977dc63ee550c3346d4ce24b
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Fri May 15 01:10:47 2026 +0200
docs: add docker nightly tagging design
commit ecc1fd9a88a0ee1dca2f1977dfc17f74527fe1da
Author: Amruth Pillai <im.amruth@gmail.com>
Date: Thu May 14 20:05:44 2026 +0200
feat: migrate to hono spa server
This commit is contained in:
Amruth Pillai
@@ -0,0 +1,281 @@
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import type { AIProvider } from "@reactive-resume/ai/types";
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import type { ResumeAnalysis } from "@reactive-resume/schema/resume/analysis";
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import type { ResumeData } from "@reactive-resume/schema/resume/data";
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import type { ModelMessage, UIMessage } from "ai";
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import { createAnthropic } from "@ai-sdk/anthropic";
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import { createGoogleGenerativeAI } from "@ai-sdk/google";
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import { createOpenAI } from "@ai-sdk/openai";
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import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
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import { streamToEventIterator } from "@orpc/server";
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import { convertToModelMessages, createGateway, generateText, Output, stepCountIs, streamText, tool } from "ai";
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import { createOllama } from "ollama-ai-provider-v2";
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import { match } from "ts-pattern";
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import { z } from "zod";
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import {
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analyzeResumeSystemPrompt as analyzeResumeSystemPromptTemplate,
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chatSystemPromptTemplate,
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docxParserSystemPrompt,
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docxParserUserPrompt,
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pdfParserSystemPrompt,
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pdfParserUserPrompt,
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} from "@reactive-resume/ai/prompts";
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import { buildAiExtractionTemplate } from "@reactive-resume/ai/resume/extraction-template";
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import { sanitizeAndParseResumeJson } from "@reactive-resume/ai/resume/sanitize";
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import {
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normalizeResumePatchProposals,
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resumePatchProposalToolInputSchema,
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resumePatchProposalToolOutputSchema,
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} from "@reactive-resume/ai/tools/patch-proposal";
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import { aiProviderSchema } from "@reactive-resume/ai/types";
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import { applyResumePatches } from "@reactive-resume/resume/patch";
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import { resumeAnalysisOutputSchema, resumeAnalysisSchema } from "@reactive-resume/schema/resume/analysis";
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import { supportsProviderNativeWebSearch } from "./capabilities";
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import { resolveAiBaseUrl } from "./url-policy";
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const aiExtractionTemplate = buildAiExtractionTemplate();
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function logAndRethrow(context: string, error: unknown): never {
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if (error instanceof Error) {
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console.error(`${context}:`, error);
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throw error;
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}
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console.error(`${context}:`, error);
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throw new Error(`An unknown error occurred during ${context}.`);
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}
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function parseAndValidateResumeJson(resultText: string): ResumeData {
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const { data, diagnostics } = sanitizeAndParseResumeJson(resultText);
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if (diagnostics.coercions.length === 0 && diagnostics.droppedSectionItems.length === 0) return data;
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const droppedBySection = diagnostics.droppedSectionItems.reduce<Record<string, number>>((acc, item) => {
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acc[item.section] = (acc[item.section] ?? 0) + 1;
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return acc;
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}, {});
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console.info("AI resume sanitization diagnostics", {
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coercions: diagnostics.coercions.length,
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droppedBySection,
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salvageApplied: diagnostics.salvageApplied,
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});
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return data;
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}
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type GetModelInput = {
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provider: AIProvider;
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model: string;
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apiKey: string;
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baseURL?: string;
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};
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const MAX_AI_FILE_BYTES = 10 * 1024 * 1024; // 10MB
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const MAX_AI_FILE_BASE64_CHARS = Math.ceil((MAX_AI_FILE_BYTES * 4) / 3) + 4;
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export function getModel(input: GetModelInput) {
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const { provider, model, apiKey } = input;
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const baseURL = resolveAiBaseUrl(input);
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return match(provider)
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.with("openai", () => createOpenAI({ apiKey, baseURL }).chat(model))
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.with("anthropic", () => createAnthropic({ apiKey, baseURL }).languageModel(model))
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.with("gemini", () => createGoogleGenerativeAI({ apiKey, baseURL }).languageModel(model))
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.with("vercel-ai-gateway", () => createGateway({ apiKey, baseURL }).languageModel(model))
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.with("openrouter", () => createOpenAICompatible({ name: "openrouter", apiKey, baseURL }).languageModel(model))
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.with("openai-compatible", () =>
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createOpenAICompatible({ name: "openai-compatible", apiKey, baseURL }).languageModel(model),
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)
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.with("ollama", () => {
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const ollama = createOllama({
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name: "ollama",
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baseURL,
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...(apiKey ? { headers: { Authorization: `Bearer ${apiKey}` } } : {}),
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});
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return ollama.languageModel(model);
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})
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.exhaustive();
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}
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export function getAgentModel(input: GetModelInput) {
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if (!supportsProviderNativeWebSearch(input)) return getModel(input);
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return createOpenAI({ apiKey: input.apiKey, baseURL: resolveAiBaseUrl(input) }).responses(input.model);
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}
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const aiCredentialsSchema = z.object({
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provider: aiProviderSchema,
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model: z.string().trim().min(1),
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apiKey: z.string().trim().min(1),
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baseURL: z.string().optional().default(""),
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});
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export const fileInputSchema = z.object({
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name: z.string(),
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data: z.string().max(MAX_AI_FILE_BASE64_CHARS, "File is too large. Maximum size is 10MB."),
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});
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type TestConnectionInput = z.infer<typeof aiCredentialsSchema>;
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export async function testConnection(input: TestConnectionInput): Promise<boolean> {
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const RESPONSE_OK = "1";
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const result = await generateText({
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model: getModel(input),
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output: Output.choice({ options: [RESPONSE_OK] }),
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messages: [{ role: "user", content: `Respond only with JSON Object: { "result": "${RESPONSE_OK}" }` }],
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});
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return result.output === RESPONSE_OK;
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}
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type ParsePdfInput = z.infer<typeof aiCredentialsSchema> & {
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file: z.infer<typeof fileInputSchema>;
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};
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type BuildResumeParsingMessagesInput = {
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systemPrompt: string;
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userPrompt: string;
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file: z.infer<typeof fileInputSchema>;
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mediaType: string;
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};
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function buildResumeParsingMessages({
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systemPrompt,
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userPrompt,
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file,
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mediaType,
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}: BuildResumeParsingMessagesInput): ModelMessage[] {
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return [
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{
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role: "system",
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content: `${systemPrompt}\n\nIMPORTANT: You must return ONLY raw valid JSON. Do not return markdown, do not return explanations. Just the JSON object. Use the following JSON as a template and fill in the extracted values. For arrays, you MUST use the exact key names shown in the template (e.g. use 'description' instead of 'summary', 'website' instead of 'url'):\n\n${JSON.stringify(aiExtractionTemplate, null, 2)}`,
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},
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{
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role: "user",
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content: [
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{ type: "text", text: userPrompt },
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{ type: "file", data: file.data, mediaType, filename: file.name },
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],
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},
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];
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}
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async function parsePdf(input: ParsePdfInput): Promise<ResumeData> {
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const model = getModel(input);
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const result = await generateText({
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model,
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messages: buildResumeParsingMessages({
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systemPrompt: pdfParserSystemPrompt,
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userPrompt: pdfParserUserPrompt,
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file: input.file,
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mediaType: "application/pdf",
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}),
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}).catch((error: unknown) => logAndRethrow("Failed to generate the text with the model", error));
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return parseAndValidateResumeJson(result.text);
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}
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type ParseDocxInput = z.infer<typeof aiCredentialsSchema> & {
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file: z.infer<typeof fileInputSchema>;
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mediaType: "application/msword" | "application/vnd.openxmlformats-officedocument.wordprocessingml.document";
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};
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async function parseDocx(input: ParseDocxInput): Promise<ResumeData> {
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const model = getModel(input);
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const result = await generateText({
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model,
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messages: buildResumeParsingMessages({
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systemPrompt: docxParserSystemPrompt,
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userPrompt: docxParserUserPrompt,
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file: input.file,
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mediaType: input.mediaType,
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}),
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}).catch((error: unknown) => logAndRethrow("Failed to generate the text with the model", error));
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return parseAndValidateResumeJson(result.text);
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}
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function buildChatSystemPrompt(resumeData: ResumeData): string {
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return chatSystemPromptTemplate.replace("{{RESUME_DATA}}", JSON.stringify(resumeData, null, 2));
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}
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type ChatInput = z.infer<typeof aiCredentialsSchema> & {
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messages: UIMessage[];
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resumeData: ResumeData;
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resumeUpdatedAt: Date;
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};
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async function chat(input: ChatInput) {
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const model = getModel(input);
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const systemPrompt = buildChatSystemPrompt(input.resumeData);
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const result = streamText({
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model,
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system: systemPrompt,
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messages: await convertToModelMessages(input.messages),
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tools: {
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propose_resume_patches: tool({
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description:
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"Return one or more cohesive resume change proposals. Each proposal must include a title, optional summary, and valid JSON Patch operations against the current resume data. The tool validates but does not apply changes.",
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inputSchema: resumePatchProposalToolInputSchema,
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outputSchema: resumePatchProposalToolOutputSchema,
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execute: async (toolInput) => {
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const proposals = normalizeResumePatchProposals(toolInput, input.resumeUpdatedAt);
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for (const proposal of proposals) {
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applyResumePatches(input.resumeData, proposal.operations);
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}
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return { proposals };
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},
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}),
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},
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stopWhen: stepCountIs(3),
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});
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return streamToEventIterator(result.toUIMessageStream());
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}
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type AnalyzeResumeInput = z.infer<typeof aiCredentialsSchema> & {
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resumeData: ResumeData;
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};
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function buildAnalyzeResumeSystemPrompt(resumeData: ResumeData): string {
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return `${analyzeResumeSystemPromptTemplate}\n\n## Resume Data\n\n${JSON.stringify(resumeData, null, 2)}`;
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}
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async function analyzeResume(input: AnalyzeResumeInput): Promise<ResumeAnalysis> {
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const model = getModel(input);
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const systemPrompt = buildAnalyzeResumeSystemPrompt(input.resumeData);
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const result = await generateText({
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model,
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output: Output.object({ schema: resumeAnalysisOutputSchema }),
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messages: [
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{ role: "system", content: systemPrompt },
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{
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role: "user",
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content:
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"Analyze this resume and return a structured report with scorecard, overall score, strengths, and actionable suggestions.",
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},
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],
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});
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if (result.output == null) {
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throw new Error("AI returned no structured analysis output.");
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}
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return resumeAnalysisSchema.parse(result.output);
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}
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export const aiService = {
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analyzeResume,
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chat,
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parseDocx,
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parsePdf,
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testConnection,
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};
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