Generate Action Items with LLM Gateway

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import requests
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import time
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API_KEY = "YOUR_API_KEY"
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audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
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# Step 1: Upload or provide audio URL and start transcription
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transcript_request = requests.post(
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    "https://api.assemblyai.com/v2/transcript",
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    headers={"authorization": API_KEY, "content-type": "application/json"},
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    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
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)
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transcript_id = transcript_request.json()["id"]
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# Step 2: Poll until transcription completes
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while True:
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    polling_response = requests.get(
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        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
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        headers={"authorization": API_KEY},
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    )
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    status = polling_response.json()["status"]
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    if status == "completed":
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        transcript_text = polling_response.json()["text"]
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        break
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    elif status == "error":
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        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
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    else:
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        print(f"Transcription status: {status}")
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        time.sleep(3)
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# Step 3: Build the prompt
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prompt = """
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Here are guidelines to follow:
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- You are an expert at understanding transcripts of conversations, calls and meetings.
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- You are an expert at coming up with ideal action items based on the contents of the transcripts.
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- Action items are things that the transcript implies should get done.
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- Your action item ideas do not make stuff up that isn't relevant to the transcript.
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- You do not needlessly make up action items - you stick to important tasks.
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- You are useful, true and concise, and write in perfect English.
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- Your action items can be tied back to direct quotes in the transcript.
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- You do not cite the quotes the action items relate to.
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- The action items are written succinctly.
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- Please give useful action items based on the transcript.
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"""
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answer_format = "Bullet Points"
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if answer_format:
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    prompt += f"\nYour response should have the following format: {answer_format}"
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# Step 4: Send transcript text to LLM Gateway
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headers = {"authorization": API_KEY}
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response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [
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            {
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                "role": "user",
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                "content": f"{prompt}\n\nTranscript:\n{transcript_text}",
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            }
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        ],
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        "max_tokens": 1000,
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    },
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)
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# Step 5: Print the LLM-generated action items
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response_json = response.json()
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print(response_json["choices"][0]["message"]["content"])

1
import requests
2
import time
3
4
API_KEY = "YOUR_API_KEY"

1
# Step 1: Upload or provide audio URL and start transcription
2
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
3
4
transcript_request = requests.post(
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    "https://api.assemblyai.com/v2/transcript",
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    headers={"authorization": API_KEY, "content-type": "application/json"},
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    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
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)
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transcript_id = transcript_request.json()["id"]
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# Step 2: Poll until transcription completes
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while True:
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    polling_response = requests.get(
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        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
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        headers={"authorization": API_KEY},
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    )
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    status = polling_response.json()["status"]
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    if status == "completed":
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        transcript_text = polling_response.json()["text"]
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        break
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    elif status == "error":
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        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
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    else:
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        print(f"Transcription status: {status}")
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        time.sleep(3)

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prompt = f"""
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        Here are guidelines to follow:
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        - You are an expert at understanding transcripts of conversations, calls and meetings.
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        - You are an expert at coming up with ideal action items based on the contents of the transcripts.
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        - Action items are things that the transcript implies should get done.
6
        - Your action item ideas do not make stuff up that isn't relevant to the transcript.
7
        - You do not needlessly make up action items - you stick to important tasks.
8
        - You are useful, true and concise, and write in perfect English.
9
        - Your action items can be tied back to direct quotes in the transcript.
10
        - You do not cite the quotes the action items relate to.
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        - The action items are written succinctly.
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        - Please give useful action items based on the transcript.
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        - Your response should be formatted in bullet points.
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        """

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answer_format = "Bullet Points"
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if answer_format:
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    prompt += f"\nYour response should have the following format: {answer_format}"

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# Step 4: Send transcript text to LLM Gateway
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headers = {"authorization": API_KEY}
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response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [
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            {
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                "role": "user",
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                "content": f"{prompt}\n\nTranscript:\n{transcript_text}",
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            }
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        ],
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        "max_tokens": 1000,
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    },
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)
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# Step 5: Print the LLM-generated action items
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response_json = response.json()
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print(response_json["choices"][0]["message"]["content"])