> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Custom Topic Tags Using LLM Gateway
In this guide we will show you how to label content with custom topic tags using AssemblyAI's LLM Gateway.
## Quickstart
```python expandable theme={null}
import requests
import time
base_url = "https://api.assemblyai.com"
headers = {"authorization": ""}
# Step 1: Transcribe the audio
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}
response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
transcript_id = response.json()['id']
polling_endpoint = base_url + "/v2/transcript/" + transcript_id
while True:
transcription_result = requests.get(polling_endpoint, headers=headers).json()
if transcription_result['status'] == 'completed':
break
elif transcription_result['status'] == 'error':
raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
else:
time.sleep(3)
# Step 2: Generate topic tags with LLM Gateway
tag_list = {
'Sports': 'News and updates on various athletic events, teams, and sports personalities.',
'Politics': 'Coverage and discussion of government activities, policies, and political events.',
'Entertainment': 'Information on movies, music, television, celebrities, and arts.',
'Technology': 'News and reviews on gadgets, software, tech advancements, and trends.',
'Health': 'Articles focusing on medical news, wellness, and health-related topics.',
'Business': 'Updates on markets, industries, companies, and economic trends.',
'Science': 'News and insights into scientific discoveries, research, and innovations.',
'Education': 'Coverage of topics related to schools, educational policies, and learning.',
'Travel': 'Information on destinations, travel tips, and tourism news.',
'Lifestyle': 'Articles on fashion, hobbies, personal interests, and daily life.',
'Environment': 'News and discussion about environmental issues and sustainability.',
'Finance': 'Information on personal finance, investments, banking, and economic news.',
'World News': 'International news covering global events and issues.',
'Crime': 'Reports and updates on criminal activities, law enforcement, and legal cases.',
'Culture': 'Coverage of cultural events, traditions, and societal norms.'
}
prompt = f"""
You are a helpful assistant designed to label video content with topic tags.
I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
{tag_list}
"""
llm_gateway_data = {
"model": "claude-sonnet-4-5-20250929",
"messages": [
{"role": "user", "content": f"{prompt}\n\n{{{{ transcript }}}}"}
],
"transcript_id": transcript_id,
"max_tokens": 500
}
response = requests.post(
"https://llm-gateway.assemblyai.com/v1/chat/completions",
headers=headers,
json=llm_gateway_data
)
result = response.json()["choices"][0]["message"]["content"]
print(result.strip())
```
```javascript expandable theme={null}
const baseUrl = "https://api.assemblyai.com";
const headers = { authorization: "" };
// Step 1: Transcribe the audio
const audioUrl = "https://storage.googleapis.com/aai-web-samples/meeting.mp4";
const data = { audio_url: audioUrl, speech_models: ["universal-3-pro"] };
let res = await fetch(baseUrl + "/v2/transcript", {
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;
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") {
break;
} else if (transcriptionResult.status === "error") {
throw new Error(`Transcription failed: ${transcriptionResult.error}`);
} else {
await new Promise((resolve) => setTimeout(resolve, 3000));
}
}
// Step 2: Generate topic tags with LLM Gateway
const tagList = {
Sports: "News and updates on various athletic events, teams, and sports personalities.",
Politics: "Coverage and discussion of government activities, policies, and political events.",
Entertainment: "Information on movies, music, television, celebrities, and arts.",
Technology: "News and reviews on gadgets, software, tech advancements, and trends.",
Health: "Articles focusing on medical news, wellness, and health-related topics.",
Business: "Updates on markets, industries, companies, and economic trends.",
Science: "News and insights into scientific discoveries, research, and innovations.",
Education: "Coverage of topics related to schools, educational policies, and learning.",
Travel: "Information on destinations, travel tips, and tourism news.",
Lifestyle: "Articles on fashion, hobbies, personal interests, and daily life.",
Environment: "News and discussion about environmental issues and sustainability.",
Finance: "Information on personal finance, investments, banking, and economic news.",
"World News": "International news covering global events and issues.",
Crime: "Reports and updates on criminal activities, law enforcement, and legal cases.",
Culture: "Coverage of cultural events, traditions, and societal norms.",
};
const prompt = `
You are a helpful assistant designed to label video content with topic tags.
I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
${JSON.stringify(tagList, null, 2)}
`;
const llmGatewayData = {
model: "claude-sonnet-4-5-20250929",
messages: [
{ role: "user", content: `${prompt}\n\n{{ transcript }}` },
],
transcript_id: transcriptId,
max_tokens: 500,
};
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());
```
## Get Started
Before we begin, make sure you have an AssemblyAI account and an API key. You can [sign up for an AssemblyAI account](https://www.assemblyai.com/app) and get your API key from your dashboard.
## Step-by-Step Instructions
### Install dependencies
Install the required packages:
```bash theme={null}
pip install requests
```
Set up your API client and transcribe the audio file:
```python expandable theme={null}
import requests
import time
base_url = "https://api.assemblyai.com"
headers = {"authorization": ""}
# Transcribe the audio
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]} # You can also use a URL to an audio or video file on the web
response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
transcript_id = response.json()['id']
polling_endpoint = base_url + "/v2/transcript/" + transcript_id
while True:
transcription_result = requests.get(polling_endpoint, headers=headers).json()
if transcription_result['status'] == 'completed':
break
elif transcription_result['status'] == 'error':
raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
else:
time.sleep(3)
```
```javascript expandable theme={null}
const baseUrl = "https://api.assemblyai.com";
const headers = { authorization: "" };
// Transcribe the audio
const audioUrl = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"; // You can also use a URL to an audio or video file on the web
const data = { audio_url: audioUrl, speech_models: ["universal-3-pro"] };
let res = await fetch(baseUrl + "/v2/transcript", {
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;
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") {
break;
} else if (transcriptionResult.status === "error") {
throw new Error(`Transcription failed: ${transcriptionResult.error}`);
} else {
await new Promise((resolve) => setTimeout(resolve, 3000));
}
}
```
Create a `tag_list` of custom topics, which consists of a key that is the topic and a value that is a short description of what qualifies a file to be labeled with that topic.
Here is an example of a `tag_list` that can be used for videos or podcasts:
```python theme={null}
tag_list = {
'Sports': 'News and updates on various athletic events, teams, and sports personalities.',
'Politics': 'Coverage and discussion of government activities, policies, and political events.',
'Entertainment': 'Information on movies, music, television, celebrities, and arts.',
'Technology': 'News and reviews on gadgets, software, tech advancements, and trends.',
'Health': 'Articles focusing on medical news, wellness, and health-related topics.',
'Business': 'Updates on markets, industries, companies, and economic trends.',
'Science': 'News and insights into scientific discoveries, research, and innovations.',
'Education': 'Coverage of topics related to schools, educational policies, and learning.',
'Travel': 'Information on destinations, travel tips, and tourism news.',
'Lifestyle': 'Articles on fashion, hobbies, personal interests, and daily life.',
'Environment': 'News and discussion about environmental issues and sustainability.',
'Finance': 'Information on personal finance, investments, banking, and economic news.',
'World News': 'International news covering global events and issues.',
'Crime': 'Reports and updates on criminal activities, law enforcement, and legal cases.',
'Culture': 'Coverage of cultural events, traditions, and societal norms.'
}
```
Create a `tagList` of custom topics, which consists of a key that is the topic and a value that is a short description of what qualifies a file to be labeled with that topic.
Here is an example of a `tagList` that can be used for videos or podcasts:
```javascript theme={null}
const tagList = {
Sports: "News and updates on various athletic events, teams, and sports personalities.",
Politics: "Coverage and discussion of government activities, policies, and political events.",
Entertainment: "Information on movies, music, television, celebrities, and arts.",
Technology: "News and reviews on gadgets, software, tech advancements, and trends.",
Health: "Articles focusing on medical news, wellness, and health-related topics.",
Business: "Updates on markets, industries, companies, and economic trends.",
Science: "News and insights into scientific discoveries, research, and innovations.",
Education: "Coverage of topics related to schools, educational policies, and learning.",
Travel: "Information on destinations, travel tips, and tourism news.",
Lifestyle: "Articles on fashion, hobbies, personal interests, and daily life.",
Environment: "News and discussion about environmental issues and sustainability.",
Finance: "Information on personal finance, investments, banking, and economic news.",
"World News": "International news covering global events and issues.",
Crime: "Reports and updates on criminal activities, law enforcement, and legal cases.",
Culture: "Coverage of cultural events, traditions, and societal norms.",
};
```
Here is another example of a `tag_list` that can be used for support calls:
```python theme={null}
tag_list = {
'Account Issues': 'Problems related to user accounts, such as login difficulties or account access.',
'Technical Support': 'Inquiries regarding software or hardware functionality and troubleshooting.',
'Billing and Payments': 'Questions or problems about invoices, payments, or subscription plans.',
'Product Inquiry': 'Requests for information about product features, capabilities, or availability.',
'Service Disruption': 'Reports of outages or interruptions in service performance or availability.'
}
```
Here is another example of a `tagList` that can be used for support calls:
```javascript theme={null}
const tagList = {
"Account Issues": "Problems related to user accounts, such as login difficulties or account access.",
"Technical Support": "Inquiries regarding software or hardware functionality and troubleshooting.",
"Billing and Payments": "Questions or problems about invoices, payments, or subscription plans.",
"Product Inquiry": "Requests for information about product features, capabilities, or availability.",
"Service Disruption": "Reports of outages or interruptions in service performance or availability.",
};
```
Use LLM Gateway to analyze the transcript and select the most relevant topic tag. This is an example prompt, which you can modify to suit your specific requirements.
```python expandable theme={null}
prompt = f"""
You are a helpful assistant designed to label video content with topic tags.
I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
{tag_list}
"""
llm_gateway_data = {
"model": "claude-sonnet-4-5-20250929",
"messages": [
{"role": "user", "content": f"{prompt}\n\n{{{{ transcript }}}}"}
],
"transcript_id": transcript_id,
"max_tokens": 500
}
response = requests.post(
"https://llm-gateway.assemblyai.com/v1/chat/completions",
headers=headers,
json=llm_gateway_data
)
result = response.json()["choices"][0]["message"]["content"]
print(result.strip())
```
```javascript expandable theme={null}
const prompt = `
You are a helpful assistant designed to label video content with topic tags.
I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
${JSON.stringify(tagList, null, 2)}
`;
const llmGatewayData = {
model: "claude-sonnet-4-5-20250929",
messages: [
{ role: "user", content: `${prompt}\n\n{{ transcript }}` },
],
transcript_id: transcriptId,
max_tokens: 500,
};
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());
```