Generate SOAP Notes using LLM Gateway

import fs from "fs-extra";

const baseUrl = "https://api.assemblyai.com";

const headers = {
  authorization: "<YOUR_API_KEY>",
};

const audioData = await fs.readFile("./local_file.mp3");
let res = await fetch(`${baseUrl}/v2/upload`, {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const uploadUrl = uploadResponse.upload_url;

const data = {
  audio_url: uploadUrl, // You can also use a URL to an audio or video file on the web
  speech_models: ["universal-3-pro"],
  redact_pii: true,
  redact_pii_policies: ["person_name", "organization", "occupation"],
  redact_pii_sub: "hash",
};

const url = `${baseUrl}/v2/transcript`;
res = await fetch(url, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const transcriptResponse = await res.json();

const transcriptId = transcriptResponse.id;
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

console.log("Transcript ID:", transcriptId);

let transcriptionResult;
while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  transcriptionResult = await res.json();

  if (transcriptionResult.status === "completed") {
    console.log(transcriptionResult.text);
    break;
  } else if (transcriptionResult.status === "error") {
    throw new Error(`Transcription failed: ${transcriptionResult.error}`);
  } else {
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}

const prompt = `
Generate a SOAP note summary based on the following doctor-patient appointment transcript.

Context: This is a doctor appointment between patient and provider. The personal identification information has been redacted.

Format the response as follows:

Subjective
This is typically the shortest section (only 2-3 sentences) and it describes the patient's affect, as the professional sees it. This information is all subjective (it isn't measureable).
- Include information that may have affected the patient's performance, such as if they were sick, tired, attentive, distractible, etc.
- Was the patient on time or did they come late?
- May include a quote of something the patient said, or how they reported feeling

Objective
This section includes factual, measurable, and objective information. This may include:
- Direct patient quotes
- Measurements
- Data on patient performance

Assessment
This section should be the meat of the SOAP note. It contains a narrative of what actually happened during the session. There may be information regarding:
- Whether improvements have been made since the last session
- Any potential barriers to success
- Clinician's interpretation of the results of the session

Plan
This is another short section that states the plan for future sessions. In most settings, this section may be bulleted
`;

// Send to LLM Gateway
const llmGatewayData = {
  model: "claude-sonnet-4-5-20250929",
  messages: [
    { role: "user", content: `${prompt}\n\n{{ transcript }}` },
  ],
  transcript_id: transcriptId,
  max_tokens: 2000,
};

let res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(llmGatewayData),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();

const result = response.choices[0].message.content;
console.log(result.trim());

// Define questions for each SOAP section
const questions = [
  {
    section: "Subjective",
    question: "What are the patient's current symptoms or concerns?",
    context: "Gather information about the patient's subjective experience. Example: The patient reports experiencing persistent headaches and dizziness.",
    format: "<patient's symptoms or concerns>, [exact quote from patient]",
  },
  {
    section: "Objective",
    question: "What are the measurable and observable findings from the examination?",
    context: "Collect data on the patient's objective signs and measurements. Example: The examination reveals an elevated body temperature and increased heart rate.",
    format: "<measurable and observable findings>",
  },
  {
    section: "Assessment",
    question: "Based on the patient's history and examination, what is your assessment or diagnosis?",
    context: "Formulate a professional assessment based on the gathered information. Example: Based on the patient's symptoms, examination, and medical history, the preliminary diagnosis is migraine.",
    format: "<assessment or diagnosis>",
  },
  {
    section: "Plan",
    question: "What is the plan of action or treatment for the patient?",
    context: "Outline the intended course of action or treatment. Example: The treatment plan includes prescribing medication, recommending rest, and scheduling a follow-up appointment in two weeks.",
    format: "<plan of action or treatment>, [exact quote from provider]",
  },
];

// Process each section
for (const q of questions) {
  const prompt = `
${q.question}

Context: This is a doctor appointment between patient and provider. The personal identification information has been redacted. ${q.context}

Answer Format: ${q.format}
`;

  const llmGatewayData = {
    model: "claude-sonnet-4-5-20250929",
    messages: [
      { role: "user", content: `${prompt}\n\n{{ transcript }}` },
    ],
    transcript_id: transcriptId,
    max_tokens: 1000,
  };

  let res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
    method: "POST",
    headers: { ...headers, "Content-Type": "application/json" },
    body: JSON.stringify(llmGatewayData),
  });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const response = await res.json();

  const result = response.choices[0].message.content;
  console.log(`${q.section}: ${q.question}`);
  console.log(result.trim());
  console.log();
}