Analyze The Sentiment Of A Customer Call using LLM Gateway

In this guide, we’ll show you how to use AssemblyAI’s LLM Gateway framework to process an audio file and then use LLM Gateway to automatically detect sentiment analysis from customer calls as “positive”, “negative”, or “neutral”. In addition, we will glean additional insights beyond these three sentiments and learn the reasoning behind these detected sentiments.

Quickstart

Python
JavaScript

import requests
import time

API_KEY = "YOUR_API_KEY"
audio_file_path = "./meeting.mp3"

# ------------------------------------------
# Step 1: Upload the audio file
# ------------------------------------------
def upload_file(filename):
    with open(filename, "rb") as f:
        upload_url = "https://api.assemblyai.com/v2/upload"
        headers = {"authorization": API_KEY}
        response = requests.post(upload_url, headers=headers, data=f)
        response.raise_for_status()
        return response.json()["upload_url"]

audio_url = upload_file(audio_file_path)
print(f"Uploaded audio file. URL: {audio_url}")

# ------------------------------------------
# Step 2: Request transcription
# ------------------------------------------
transcript_request = requests.post(
    "https://api.assemblyai.com/v2/transcript",
    headers={"authorization": API_KEY, "content-type": "application/json"},
    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
)

transcript_id = transcript_request.json()["id"]

# Poll until completed
while True:
    polling_response = requests.get(
        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
        headers={"authorization": API_KEY},
    )
    status = polling_response.json()["status"]

    if status == "completed":
        break
    elif status == "error":
        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
    else:
        print(f"Transcription status: {status}")
        time.sleep(3)

print("\nTranscription complete.\n")

# ------------------------------------------
# Step 3: Define questions
# ------------------------------------------
agent_context = "The agent is trying to get the customer to go through with the update to their car."
customer_context = "The customer is calling to check how much it would cost to update the map in his car."

answer_format = "<answer in one sentence> <reason in one sentence>"

questions = [
    {
        "question": "What was the overall sentiment of the call?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "What was the sentiment of the agent in this call?",
        "context": agent_context,
        "answer_format": answer_format,
    },
    {
        "question": "What was the sentiment of the customer in this call?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "What quote best demonstrates the customer's level of interest?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "Provide a quote from the agent that demonstrates their level of enthusiasm.",
        "context": agent_context,
        "answer_format": answer_format,
    },
]

# ------------------------------------------
# Step 4: Build prompt for the LLM
# ------------------------------------------
question_strs = []
for q in questions:
    q_str = f"Question: {q['question']}"
    if q.get("context"):
        q_str += f"\nContext: {q['context']}"
    if q.get("answer_format"):
        q_str += f"\nAnswer Format: {q['answer_format']}"
    question_strs.append(q_str)

questions_prompt = "\n\n".join(question_strs)

prompt = f"""
You are an expert at analyzing call transcripts.
Given the series of questions below, answer them accurately and concisely.
When context or answer format is provided, use it to guide your answers.

Transcript:
{{{{ transcript }}}}

Questions:
{questions_prompt}
"""

# ------------------------------------------
# Step 5: Query the LLM Gateway
# ------------------------------------------
headers = {"authorization": API_KEY}

response = requests.post(
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
    headers=headers,
    json={
        "model": "claude-sonnet-4-5-20250929",
        "messages": [{"role": "user", "content": prompt}],
        "transcript_id": transcript_id,
        "max_tokens": 2000,
    },
)

response_json = response.json()
llm_output = response_json["choices"][0]["message"]["content"]

# ------------------------------------------
# Step 6: Parse and display the results
# ------------------------------------------
print("\n--- LLM Responses ---\n")
print(llm_output)

import fs from "fs-extra";

const API_KEY = "YOUR_API_KEY";
const audioFilePath = "./meeting.mp3";
const headers = { authorization: API_KEY };

// ------------------------------------------
// Step 1: Upload the audio file
// ------------------------------------------
const audioData = await fs.readFile(audioFilePath);
let res = await fetch("https://api.assemblyai.com/v2/upload", {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const audioUrl = uploadResponse.upload_url;
console.log(`Uploaded audio file. URL: ${audioUrl}`);

// ------------------------------------------
// Step 2: Request transcription
// ------------------------------------------
res = await fetch("https://api.assemblyai.com/v2/transcript", {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify({ audio_url: audioUrl, speech_models: ["universal-3-pro"] }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const transcriptRequest = await res.json();

const transcriptId = transcriptRequest.id;

// Poll until completed
while (true) {
  res = await fetch(`https://api.assemblyai.com/v2/transcript/${transcriptId}`, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const pollingResponse = await res.json();
  const status = pollingResponse.status;

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

console.log("\nTranscription complete.\n");

// ------------------------------------------
// Step 3: Define questions
// ------------------------------------------
const agentContext = "The agent is trying to get the customer to go through with the update to their car.";
const customerContext = "The customer is calling to check how much it would cost to update the map in his car.";

const answerFormat = "<answer in one sentence> <reason in one sentence>";

const questions = [
  {
    question: "What was the overall sentiment of the call?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "What was the sentiment of the agent in this call?",
    context: agentContext,
    answer_format: answerFormat,
  },
  {
    question: "What was the sentiment of the customer in this call?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "What quote best demonstrates the customer's level of interest?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "Provide a quote from the agent that demonstrates their level of enthusiasm.",
    context: agentContext,
    answer_format: answerFormat,
  },
];

// ------------------------------------------
// Step 4: Build prompt for the LLM
// ------------------------------------------
const questionStrs = [];
for (const q of questions) {
  let qStr = `Question: ${q.question}`;
  if (q.context) {
    qStr += `\nContext: ${q.context}`;
  }
  if (q.answer_format) {
    qStr += `\nAnswer Format: ${q.answer_format}`;
  }
  questionStrs.push(qStr);
}

const questionsPrompt = questionStrs.join("\n\n");

const prompt = `
You are an expert at analyzing call transcripts.
Given the series of questions below, answer them accurately and concisely.
When context or answer format is provided, use it to guide your answers.

Transcript:
{{ transcript }}

Questions:
${questionsPrompt}
`;

// ------------------------------------------
// Step 5: Query the LLM Gateway
// ------------------------------------------
res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "claude-sonnet-4-5-20250929",
    messages: [{ role: "user", content: prompt }],
    transcript_id: transcriptId,
    max_tokens: 2000,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();

const llmOutput = response.choices[0].message.content;

// ------------------------------------------
// Step 6: Parse and display the results
// ------------------------------------------
console.log("\n--- LLM Responses ---\n");
console.log(llmOutput);

Get Started

Before we begin, make sure you have an AssemblyAI account and an API key. You can sign up for an AssemblyAI account and get your API key from your dashboard. See our pricing page for LLM Gateway pricing rates.

Step-by-Step Instructions

In this guide, we will ask five questions to learn about the sentiment of the customer and agent. You can adjust the questions to suit your project’s needs.

Install dependencies

Install the required packages.

Python
JavaScript

pip install requests

npm install fs-extra

Import the required libraries and set your AssemblyAI API key.

Python
JavaScript

import requests
import time

API_KEY = "YOUR_API_KEY"

import fs from "fs-extra";

const API_KEY = "YOUR_API_KEY";

Next, you’ll upload your audio file to AssemblyAI’s servers. Once the upload is complete, the API will return a temporary URL that can be used to start the transcription. After submitting the transcription request, your script will poll the API until the transcription is finished.

Python
JavaScript

audio_file_path = "./meeting.mp3"
# ------------------------------------------
# Step 1: Upload the audio file
# ------------------------------------------
def upload_file(filename):
    with open(filename, "rb") as f:
        upload_url = "https://api.assemblyai.com/v2/upload"
        headers = {"authorization": API_KEY}
        response = requests.post(upload_url, headers=headers, data=f)
        response.raise_for_status()
        return response.json()["upload_url"]
audio_url = upload_file(audio_file_path)
print(f"Uploaded audio file. URL: {audio_url}")
# ------------------------------------------
# Step 2: Request transcription
# ------------------------------------------
transcript_request = requests.post(
    "https://api.assemblyai.com/v2/transcript",
    headers={"authorization": API_KEY, "content-type": "application/json"},
    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
)
transcript_id = transcript_request.json()["id"]
# Poll until completed
while True:
    polling_response = requests.get(
        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
        headers={"authorization": API_KEY},
    )
    status = polling_response.json()["status"]
    if status == "completed":
        break
    elif status == "error":
        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
    else:
        print(f"Transcription status: {status}")
        time.sleep(3)
print("\nTranscription complete.\n")

const audioFilePath = "./meeting.mp3";
const headers = { authorization: API_KEY };
// ------------------------------------------
// Step 1: Upload the audio file
// ------------------------------------------
const audioData = await fs.readFile(audioFilePath);
let res = await fetch("https://api.assemblyai.com/v2/upload", {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const audioUrl = uploadResponse.upload_url;
console.log(`Uploaded audio file. URL: ${audioUrl}`);
// ------------------------------------------
// Step 2: Request transcription
// ------------------------------------------
res = await fetch("https://api.assemblyai.com/v2/transcript", {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify({ audio_url: audioUrl, speech_models: ["universal-3-pro"] }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const transcriptRequest = await res.json();
const transcriptId = transcriptRequest.id;
// Poll until completed
while (true) {
  res = await fetch(`https://api.assemblyai.com/v2/transcript/${transcriptId}`, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const pollingResponse = await res.json();
  const status = pollingResponse.status;
  if (status === "completed") {
    break;
  } else if (status === "error") {
    throw new Error(`Transcription failed: ${pollingResponse.error}`);
  } else {
    console.log(`Transcription status: ${status}`);
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}
console.log("\nTranscription complete.\n");

Once you have the transcript, you’ll define short context strings for both the agent and the customer. These will help the model better understand the roles and perspectives in the conversation.

Python
JavaScript

agent_context = "The agent is trying to get the customer to go through with the update to their car."
customer_context = "The customer is calling to check how much it would cost to update the map in his car."

const agentContext = "The agent is trying to get the customer to go through with the update to their car.";
const customerContext = "The customer is calling to check how much it would cost to update the map in his car.";

You can now specify the exact questions you want the LLM Gateway to answer. Each question can include optional context and an answer format that tells the model how to structure its response.

Python
JavaScript

answer_format = "<answer in one sentence> <reason in one sentence>"
questions = [
    {
        "question": "What was the overall sentiment of the call?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "What was the sentiment of the agent in this call?",
        "context": agent_context,
        "answer_format": answer_format,
    },
    {
        "question": "What was the sentiment of the customer in this call?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "What quote best demonstrates the customer's level of interest?",
        "context": customer_context,
        "answer_format": answer_format,
    },
    {
        "question": "Provide a quote from the agent that demonstrates their level of enthusiasm.",
        "context": agent_context,
        "answer_format": answer_format,
    },
]

const answerFormat = "<answer in one sentence> <reason in one sentence>";
const questions = [
  {
    question: "What was the overall sentiment of the call?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "What was the sentiment of the agent in this call?",
    context: agentContext,
    answer_format: answerFormat,
  },
  {
    question: "What was the sentiment of the customer in this call?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "What quote best demonstrates the customer's level of interest?",
    context: customerContext,
    answer_format: answerFormat,
  },
  {
    question: "Provide a quote from the agent that demonstrates their level of enthusiasm.",
    context: agentContext,
    answer_format: answerFormat,
  },
];

Now that the questions are defined, combine them into a single formatted prompt. This prompt includes both the call transcript and the questions you want the model to address. The model will use these details to generate accurate and concise responses.

Python
JavaScript

# ------------------------------------------
# Step 4: Build prompt for the LLM
# ------------------------------------------
question_strs = []
for q in questions:
    q_str = f"Question: {q['question']}"
    if q.get("context"):
        q_str += f"\nContext: {q['context']}"
    if q.get("answer_format"):
        q_str += f"\nAnswer Format: {q['answer_format']}"
    question_strs.append(q_str)
questions_prompt = "\n\n".join(question_strs)
prompt = f"""
You are an expert at analyzing call transcripts.
Given the series of questions below, answer them accurately and concisely.
When context or answer format is provided, use it to guide your answers.
Transcript:
{{{{ transcript }}}}
Questions:
{questions_prompt}
"""

// ------------------------------------------
// Step 4: Build prompt for the LLM
// ------------------------------------------
const questionStrs = [];
for (const q of questions) {
  let qStr = `Question: ${q.question}`;
  if (q.context) {
    qStr += `\nContext: ${q.context}`;
  }
  if (q.answer_format) {
    qStr += `\nAnswer Format: ${q.answer_format}`;
  }
  questionStrs.push(qStr);
}
const questionsPrompt = questionStrs.join("\n\n");
const prompt = `
You are an expert at analyzing call transcripts.
Given the series of questions below, answer them accurately and concisely.
When context or answer format is provided, use it to guide your answers.
Transcript:
{{ transcript }}
Questions:
${questionsPrompt}
`;

With the prompt prepared, query LLM Gateway then extract and print the answers returned by the LLM Gateway. This step displays the model’s assessment of each question, including the identified sentiments and their reasoning.

Python
JavaScript

# ------------------------------------------
# Step 5: Query the LLM Gateway
# ------------------------------------------
headers = {"authorization": API_KEY}
response = requests.post(
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
    headers=headers,
    json={
        "model": "claude-sonnet-4-5-20250929",
        "messages": [{"role": "user", "content": prompt}],
        "transcript_id": transcript_id,
        "max_tokens": 2000,
    },
)
response_json = response.json()
llm_output = response_json["choices"][0]["message"]["content"]
# ------------------------------------------
# Step 6: Parse and display the results
# ------------------------------------------
print("\n--- LLM Responses ---\n")
print(llm_output)

// ------------------------------------------
// Step 5: Query the LLM Gateway
// ------------------------------------------
let res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "claude-sonnet-4-5-20250929",
    messages: [{ role: "user", content: prompt }],
    transcript_id: transcriptId,
    max_tokens: 2000,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();
const llmOutput = response.choices[0].message.content;
// ------------------------------------------
// Step 6: Parse and display the results
// ------------------------------------------
console.log("\n--- LLM Responses ---\n");
console.log(llmOutput);

Documentation Index

​Quickstart

​Get Started

​Step-by-Step Instructions

​Install dependencies

Quickstart

Get Started

Step-by-Step Instructions

Install dependencies