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https://github.com/AmruthPillai/Reactive-Resume.git
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v5.1.0 (#2970)
* chore(release): v5.1.0 * feat: implement resume thumbnails * fix: remove unused mcp tools * docs: fix formatting of docs
This commit is contained in:
Amruth Pillai
GitHub
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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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executePatchResume,
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patchResumeDescription,
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patchResumeInputSchema,
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} from "@reactive-resume/ai/tools/patch-resume";
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import { AI_PROVIDER_DEFAULT_BASE_URLS, aiProviderSchema } from "@reactive-resume/ai/types";
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import { resumeAnalysisOutputSchema, resumeAnalysisSchema } from "@reactive-resume/schema/resume/analysis";
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import { isPrivateOrLoopbackHost, parseUrl } from "@reactive-resume/utils/url-security";
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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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function resolveBaseUrl(input: GetModelInput): string {
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const baseURL = input.baseURL?.trim() || AI_PROVIDER_DEFAULT_BASE_URLS[input.provider];
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if (!baseURL) throw new Error("INVALID_AI_BASE_URL");
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const parsedBaseURL = parseUrl(baseURL);
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if (!parsedBaseURL) throw new Error("INVALID_AI_BASE_URL");
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if (parsedBaseURL.protocol !== "https:") throw new Error("INVALID_AI_BASE_URL");
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if (parsedBaseURL.username || parsedBaseURL.password) throw new Error("INVALID_AI_BASE_URL");
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if (isPrivateOrLoopbackHost(parsedBaseURL.hostname)) throw new Error("INVALID_AI_BASE_URL");
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return parsedBaseURL.toString();
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}
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function getModel(input: GetModelInput) {
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const { provider, model, apiKey } = input;
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const baseURL = resolveBaseUrl(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("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 ${apiKey}` } } : {}),
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});
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return ollama.languageModel(model);
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})
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.exhaustive();
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}
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export const aiCredentialsSchema = z.object({
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provider: aiProviderSchema,
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model: z.string().trim().min(1),
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apiKey: z.string().trim().min(1),
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baseURL: z.string().optional().default(""),
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});
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export const fileInputSchema = z.object({
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name: z.string(),
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data: z.string().max(MAX_AI_FILE_BASE64_CHARS, "File is too large. Maximum size is 10MB."),
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});
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type TestConnectionInput = z.infer<typeof aiCredentialsSchema>;
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async function testConnection(input: TestConnectionInput): Promise<boolean> {
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const RESPONSE_OK = "1";
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const result = await generateText({
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model: getModel(input),
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output: Output.choice({ options: [RESPONSE_OK] }),
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messages: [{ role: "user", content: `Respond only with JSON Object: { "result": "${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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};
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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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patch_resume: tool({
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description: patchResumeDescription,
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inputSchema: patchResumeInputSchema,
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execute: async ({ operations }) => executePatchResume(input.resumeData, operations),
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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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