GiteadandClaude Sonnet 4.6
ba2882957a
- Add eligibility_by_id and check_and_claim intents to internal chat - New cdt-lookup.ts: keyword search against fee schedule JSON (no LLM) - New internal-chat-workflow.ts: deterministic orchestration — patient resolution, insurance siteKey derivation, CDT code mapping - Custom CDT aliases stored per-user in DB (TwilioSettings JSON blob) with GET/PUT /api/ai/cdt-aliases endpoints - Chatbot UI: new steps for eligibility-id-ready, check-and-claim-ready, and need-insurance-clarification with insurance picker - Settings UI: CDT Aliases CRUD table with built-in alias reference Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
102 lines
4.8 KiB
TypeScript
102 lines
4.8 KiB
TypeScript
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
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// ─── Intent types ─────────────────────────────────────────────────────────────
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export type InternalChatIntent =
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| "check_eligibility" // by patient name → look up in DB
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| "eligibility_by_id" // by explicit memberId + dob (no name)
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| "check_and_claim" // eligibility + claim procedures
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| "find_patient" // look up patient record only
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| "navigate_claims"
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| "navigate_schedule"
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| "general";
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export interface ChatClassification {
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intent: InternalChatIntent;
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// --- patient resolution (one of name OR id+dob) ---
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patientName?: string; // for check_eligibility / find_patient
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memberId?: string; // for eligibility_by_id / check_and_claim
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dob?: string; // for eligibility_by_id / check_and_claim (MM/DD/YYYY)
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// --- insurance hint (only if explicitly stated in the message) ---
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insuranceHint?: string; // raw text, e.g. "masshealth", "BCBS", "CCA"
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// --- procedures (raw text, NOT CDT codes — CDT lookup is done in workflow) ---
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procedureNames?: string[]; // for check_and_claim, e.g. ["perio exam", "adult cleaning"]
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fallbackReply: string;
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}
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// ─── System prompt ────────────────────────────────────────────────────────────
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const BASE_SYSTEM_PROMPT = `You are an internal assistant for a dental office management app.
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Staff type natural language commands. Your ONLY job is to classify the intent and extract
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structured parameters. Do NOT map procedure names to CDT codes — return them as plain text.
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Respond ONLY with valid JSON (no markdown fences):
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{
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"intent": "<intent>",
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"patientName": "<full name if mentioned by name>",
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"memberId": "<member/insurance ID if given explicitly>",
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"dob": "<date of birth in MM/DD/YYYY if given>",
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"insuranceHint": "<insurance name only if explicitly stated in the message, e.g. 'masshealth', 'BCBS MA', 'CCA'>",
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"procedureNames": ["<raw procedure name>", ...],
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"fallbackReply": "<1-2 sentence reply to show the user>"
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}
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Omit any field that is not present in the message.
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Intents:
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- check_eligibility : user wants to check insurance for a patient identified by NAME only
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e.g. "check Maria Jesus", "verify insurance for John Smith"
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- eligibility_by_id : user provides a member ID and date of birth (no patient name)
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e.g. "check masshealth for 100xxxx, 10/10/1988"
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- check_and_claim : user wants to check eligibility AND submit procedures as claims
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e.g. "check masshealth for 100xxxx, 10/10/1988 and claim perio exam and adult cleaning"
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e.g. "check Maria Jesus and claim D0120 D1110"
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- find_patient : look up a patient record only, no eligibility
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e.g. "find patient John", "look up Smith"
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- navigate_claims : open the claims page
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- navigate_schedule : open the appointments/schedule page
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- general : anything else
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Rules:
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- For check_and_claim, procedureNames should be the RAW user text
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(e.g. "perio exam", "adult cleaning", "D0120") — do NOT translate to codes
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- insuranceHint is only set when the user explicitly names an insurance in the message
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- Keep fallbackReply to 1-2 sentences
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- For navigate intents, fallbackReply = "Opening the [page] page..."`;
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// ─── Classifier ───────────────────────────────────────────────────────────────
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export async function classifyInternalChat(
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message: string,
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apiKey: string,
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extraSystemPrompt?: string
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): Promise<ChatClassification> {
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const fallback: ChatClassification = {
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intent: "general",
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fallbackReply:
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"I can search for a patient, check eligibility, run check & claim, or navigate to claims or appointments.",
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};
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if (!apiKey) return fallback;
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const systemPrompt = extraSystemPrompt
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? `${BASE_SYSTEM_PROMPT}\n\nAdditional office context:\n${extraSystemPrompt}`
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: BASE_SYSTEM_PROMPT;
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try {
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const llm = new ChatGoogleGenerativeAI({ model: "gemini-1.5-flash", apiKey });
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const response = await llm.invoke([
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{ role: "system", content: systemPrompt },
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{ role: "user", content: message },
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]);
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const raw = String(response.content).trim();
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const jsonStr = raw.replace(/^```json\s*/i, "").replace(/```\s*$/, "").trim();
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const parsed = JSON.parse(jsonStr) as ChatClassification;
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if (!parsed.intent || !parsed.fallbackReply) return fallback;
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return parsed;
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} catch {
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return fallback;
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}
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}
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