Prompt A Structured Q&A Response Using LLM Gateway

1
import requests
2
import time
3
import xml.etree.ElementTree as ET
4
5
API_KEY = "YOUR_API_KEY"
6
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
7
8
# -------------------------------
9
# Step 1: Transcribe the audio
10
# -------------------------------
11
transcript_request = requests.post(
12
    "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},
15
)
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transcript_id = transcript_request.json()["id"]
18
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# Poll for completion
20
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},
24
    )
25
    status = polling_response.json()["status"]
26
27
    if status == "completed":
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        transcript_text = polling_response.json()["text"]
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        break
30
    elif status == "error":
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        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
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    else:
33
        print(f"Transcription status: {status}")
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        time.sleep(3)
35
36
# -------------------------------
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# Step 2: Build question helper functions
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# -------------------------------
39
def construct_question(question):
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    question_str = f"Question: {question['question']}"
41
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    if question.get("context"):
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        question_str += f"\nContext: {question['context']}"
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    # Default answer_format
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    if not question.get("answer_format"):
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        question["answer_format"] = "short sentence"
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    question_str += f"\nAnswer Format: {question['answer_format']}"
50
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    if question.get("answer_options"):
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        options_str = ", ".join(question["answer_options"])
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        question_str += f"\nOptions: {options_str}"
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    return question_str + "\n"
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def escape_xml_characters(xml_string):
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    return xml_string.replace("&", "&")
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# -------------------------------
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# Step 3: Define questions
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# -------------------------------
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questions = [
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    {
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        "question": "What are the top level KPIs for engineering?",
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        "context": "KPI stands for key performance indicator",
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        "answer_format": "short sentence",
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    },
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    {
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        "question": "How many days has it been since the data team has gotten updated metrics?",
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        "answer_options": ["1", "2", "3", "4", "5", "6", "7", "more than 7"],
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    },
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    {"question": "What are the future plans for the project?"},
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]
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question_str = "\n".join(construct_question(q) for q in questions)
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# -------------------------------
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# Step 4: Build the LLM prompt
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# -------------------------------
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prompt = f"""You are an expert at giving accurate answers to questions about texts.
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No preamble.
85
Given the series of questions, answer the questions.
86
Each question may follow up with answer format, answer options, and context for each question.
87
It is critical that you follow the answer format and answer options for each question.
88
When context is provided with a question, refer to it when answering the question.
89
You are useful, true and concise, and write in perfect English.
90
Only the question is allowed between the <question> tag. Do not include the answer format, options, or question context in your response.
91
Only text is allowed between the <question> and <answer> tags.
92
XML tags are not allowed between the <question> and <answer> tags.
93
End your response with a closing </responses> tag.
94
For each question-answer pair, format your response according to the template provided below:
95
96
Template for response:
97
<responses>
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  <response>
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    <question>The question</question>
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    <answer>Your answer</answer>
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  </response>
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  <response>
103
    ...
104
  </response>
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  ...
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</responses>
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108
These are the questions:
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{question_str}
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111
Transcript:
112
{transcript_text}
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"""
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115
# -------------------------------
116
# Step 5: Query LLM Gateway
117
# -------------------------------
118
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": [{"role": "user", "content": prompt}],
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        "max_tokens": 2000,
127
    },
128
)
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response_json = response.json()
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llm_output = response_json["choices"][0]["message"]["content"]
132
133
# -------------------------------
134
# Step 6: Parse and print XML response
135
# -------------------------------
136
clean_response = escape_xml_characters(llm_output).strip()
137
138
try:
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    root = ET.fromstring(clean_response)
140
    for resp in root.findall("response"):
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        question = resp.find("question").text
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        answer = resp.find("answer").text
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        print(f"Question: {question}")
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        print(f"Answer: {answer}\n")
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except ET.ParseError as e:
146
    print("Could not parse XML response.")
147
    print("Raw model output:\n", llm_output)

1
import requests
2
import time
3
import xml.etree.ElementTree as ET
4
5
API_KEY = "YOUR_API_KEY"

1
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
2
3
# -------------------------------
4
# Step 1: Transcribe the audio
5
# -------------------------------
6
transcript_request = requests.post(
7
    "https://api.assemblyai.com/v2/transcript",
8
    headers={"authorization": API_KEY, "content-type": "application/json"},
9
    json={"audio_url": audio_url},
10
)
11
12
transcript_id = transcript_request.json()["id"]
13
14
# Poll for completion
15
while True:
16
    polling_response = requests.get(
17
        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
18
        headers={"authorization": API_KEY},
19
    )
20
    status = polling_response.json()["status"]
21
22
    if status == "completed":
23
        transcript_text = polling_response.json()["text"]
24
        break
25
    elif status == "error":
26
        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
27
    else:
28
        print(f"Transcription status: {status}")
29
        time.sleep(3)

1
# -------------------------------
2
# Step 2: Build question helper functions
3
# -------------------------------
4
def construct_question(question):
5
    question_str = f"Question: {question['question']}"
6
7
    if question.get("context"):
8
        question_str += f"\nContext: {question['context']}"
9
10
    # Default answer_format
11
    if not question.get("answer_format"):
12
        question["answer_format"] = "short sentence"
13
14
    question_str += f"\nAnswer Format: {question['answer_format']}"
15
16
    if question.get("answer_options"):
17
        options_str = ", ".join(question["answer_options"])
18
        question_str += f"\nOptions: {options_str}"
19
20
    return question_str + "\n"
21
22
def escape_xml_characters(xml_string):
23
    return xml_string.replace("&", "&")

1
# -------------------------------
2
# Step 3: Define questions
3
# -------------------------------
4
questions = [
5
    {
6
        "question": "What are the top level KPIs for engineering?",
7
        "context": "KPI stands for key performance indicator",
8
        "answer_format": "short sentence",
9
    },
10
    {
11
        "question": "How many days has it been since the data team has gotten updated metrics?",
12
        "answer_options": ["1", "2", "3", "4", "5", "6", "7", "more than 7"],
13
    },
14
    {"question": "What are the future plans for the project?"},
15
]

1
question_str = '\n'.join(construct_question(q) for q in questions)

1
# -------------------------------
2
# Step 4: Build the LLM prompt
3
# -------------------------------
4
prompt = f"""You are an expert at giving accurate answers to questions about texts.
5
No preamble.
6
Given the series of questions, answer the questions.
7
Each question may follow up with answer format, answer options, and context for each question.
8
It is critical that you follow the answer format and answer options for each question.
9
When context is provided with a question, refer to it when answering the question.
10
You are useful, true and concise, and write in perfect English.
11
Only the question is allowed between the <question> tag. Do not include the answer format, options, or question context in your response.
12
Only text is allowed between the <question> and <answer> tags.
13
XML tags are not allowed between the <question> and <answer> tags.
14
End your response with a closing </responses> tag.
15
For each question-answer pair, format your response according to the template provided below:
16
17
Template for response:
18
<responses>
19
  <response>
20
    <question>The question</question>
21
    <answer>Your answer</answer>
22
  </response>
23
  <response>
24
    ...
25
  </response>
26
  ...
27
</responses>
28
29
These are the questions:
30
{question_str}
31
32
Transcript:
33
{transcript_text}
34
"""
35
36
# -------------------------------
37
# Step 5: Query LLM Gateway
38
# -------------------------------
39
headers = {"authorization": API_KEY}
40
41
response = requests.post(
42
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
43
    headers=headers,
44
    json={
45
        "model": "claude-sonnet-4-5-20250929",
46
        "messages": [{"role": "user", "content": prompt}],
47
        "max_tokens": 2000,
48
    },
49
)
50
51
response_json = response.json()
52
llm_output = response_json["choices"][0]["message"]["content"]
53
54
# -------------------------------
55
# Step 6: Parse and print XML response
56
# -------------------------------
57
clean_response = escape_xml_characters(llm_output).strip()
58
59
try:
60
    root = ET.fromstring(clean_response)
61
    for resp in root.findall("response"):
62
        question = resp.find("question").text
63
        answer = resp.find("answer").text
64
        print(f"Question: {question}")
65
        print(f"Answer: {answer}\n")
66
except ET.ParseError as e:
67
    print("Could not parse XML response.")
68
    print("Raw model output:\n", llm_output)

1
import requests
2
import time
3
import xml.etree.ElementTree as ET
4
5
API_KEY = "YOUR_API_KEY"

1
audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
2
3
# -------------------------------
4
# Step 1: Transcribe the audio
5
# -------------------------------
6
transcript_request = requests.post(
7
    "https://api.assemblyai.com/v2/transcript",
8
    headers={"authorization": API_KEY, "content-type": "application/json"},
9
    json={"audio_url": audio_url},
10
)
11
12
transcript_id = transcript_request.json()["id"]
13
14
# Poll for completion
15
while True:
16
    polling_response = requests.get(
17
        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
18
        headers={"authorization": API_KEY},
19
    )
20
    status = polling_response.json()["status"]
21
22
    if status == "completed":
23
        transcript_text = polling_response.json()["text"]
24
        break
25
    elif status == "error":
26
        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
27
    else:
28
        print(f"Transcription status: {status}")
29
        time.sleep(3)

1
# -------------------------------
2
# Step 2: Build question helper functions
3
# -------------------------------
4
def construct_question(question):
5
    question_str = f"Question: {question.question}"
6
7
    if question.context:
8
        question_str += f"\nContext: {question.context}"
9
10
    if not question.answer_format:
11
        question.answer_format = "short sentence"
12
13
    question_str += f"\nAnswer Format: {question.answer_format}"
14
15
    if question.answer_options:
16
        options_str = ", ".join(question.answer_options)
17
        question_str += f"\nOptions: {options_str}"
18
19
    return question_str + "\n"
20
21
def escape_xml_characters(xml_string):
22
    return xml_string.replace("&", "&")

1
# -------------------------------
2
# Step 3: Define questions
3
# -------------------------------
4
questions = [
5
    {
6
        "question": "What are the top level KPIs for engineering?",
7
        "context": "KPI stands for key performance indicator",
8
        "answer_format": "short sentence",
9
    },
10
    {
11
        "question": "How many days has it been since the data team has gotten updated metrics?",
12
        "answer_options": ["1", "2", "3", "4", "5", "6", "7", "more than 7"],
13
    },
14
    {"question": "What are the future plans for the project?"},
15
]

1
question_str = '\n'.join(construct_question(q) for q in questions)

1
# -------------------------------
2
# Step 4: Build the LLM prompt
3
# -------------------------------
4
prompt = f"""You are an expert at giving accurate answers to questions about texts.
5
No preamble.
6
Given the series of questions, answer the questions.
7
Each question may follow up with answer format, answer options, and context for each question.
8
It is critical that you follow the answer format and answer options for each question.
9
When context is provided with a question, refer to it when answering the question.
10
You are useful, true and concise, and write in perfect English.
11
Only the question is allowed between the <question> tag. Do not include the answer format, options, or question context in your response.
12
Only text is allowed between the <question> and <answer> tags.
13
XML tags are not allowed between the <question> and <answer> tags.
14
End your response with a closing </responses> tag.
15
For each question-answer pair, format your response according to the template provided below:
16
17
Template for response:
18
<responses>
19
  <response>
20
    <question>The question</question>
21
    <answer>Your answer</answer>
22
  </response>
23
  <response>
24
    ...
25
  </response>
26
  ...
27
</responses>
28
29
These are the questions:
30
{question_str}
31
32
Transcript:
33
{transcript_text}
34
"""

1
# -------------------------------
2
# Step 5: Query LLM Gateway
3
# -------------------------------
4
headers = {"authorization": API_KEY}
5
6
response = requests.post(
7
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
8
    headers=headers,
9
    json={
10
        "model": "claude-sonnet-4-5-20250929",
11
        "messages": [{"role": "user", "content": prompt}],
12
        "max_tokens": 2000,
13
    },
14
)
15
16
response_json = response.json()
17
llm_output = response_json["choices"][0]["message"]["content"]

1
# -------------------------------
2
# Step 6: Parse and print XML response
3
# -------------------------------
4
clean_response = escape_xml_characters(llm_output).strip()
5
6
try:
7
    root = ET.fromstring(clean_response)
8
    for resp in root.findall("response"):
9
        question = resp.find("question").text
10
        answer = resp.find("answer").text
11
        print(f"Question: {question}")
12
        print(f"Answer: {answer}\n")
13
except ET.ParseError as e:
14
    print("Could not parse XML response.")
15
    print("Raw model output:\n", llm_output)