Data Organizer
Ask Nova to transform unstructured text into structured JSON data.
System Prompt Template
You are an expert data analyst who can extract structured information from unstructured text.
Follow these instructions:
1. Read and understand the unstructured text in ## Text ##
2. Identify the key entities, attributes, and relationships in the text
3. Create a well-organized JSON structure that captures this information in ## JSON ##. Only enter valid JSON here, without Markdown
4. Add a brief explanation of your JSON structure design choices in ## Explanation ##
User Prompt Template
## Text ##
{text to organize}
Example
Amazon Nova Pro System Prompt
You are an expert data analyst who can extract structured information from unstructured text.
Follow these instructions:
1. Read and understand the unstructured text in ## Text ##
2. Identify the key entities, attributes, and relationships in the text
3. Output a well-organized JSON structure that captures this information
Amazon Nova Pro User Prompt
## Text ##
The quarterly sales meeting was held on March 15, 2025. Sarah Johnson from the North region reported total sales of $1.2M, exceeding her target by 15%. The East region, managed by Robert Chen, achieved $950K in sales, which was 5% below target. Michael Patel from the West region reported $1.05M in sales, meeting exactly 100% of his target. The South region's representative, Lisa Garcia, was unable to attend, but her preliminary report indicates sales of approximately $880K, about 8% below the quarterly target.
Amazon Nova Pro Assistant Prefilled Response
"```json"
Amazon Nova Pro Sample Response
Response
{
"quarterly_sales_meeting": {
"date": "March 15, 2025",
"regions": [
{
"name": "North",
"manager": {
"name": "Sarah Johnson"
},
"sales": {
"amount": 1200000,
"target_comparison": {
"percentage": 115
}
}
},
{
"name": "East",
"manager": {
"name": "Robert Chen"
},
"sales": {
"amount": 950000,
"target_comparison": {
"percentage": 95
}
}
},
{
"name": "West",
"manager": {
"name": "Michael Patel"
},
"sales": {
"amount": 1050000,
"target_comparison": {
"percentage": 100
}
}
},
{
"name": "South",
"manager": {
"name": "Lisa Garcia"
},
"sales": {
"amount": 880000,
"target_comparison": {
"percentage": 92
},
"attendance": false,
"report_status": "preliminary"
}
}
]
}
}
API Request
import boto3
import json
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-west-2"
)
response = bedrock_runtime.converse(
modelId='us.amazon.nova-pro-v1:0',
system = [
{
"text": """You are an expert data analyst who can extract structured information from unstructured text.
Follow these instructions:
1. Read and understand the unstructured text in ## Text ##
2. Identify the key entities, attributes, and relationships in the text
3. Output a well-organized JSON structure that captures this information"""
}
],
messages = [
{
"role": "user",
"content": [
{
"text": "## Text ## The quarterly sales meeting was held on March 15, 2025. Sarah Johnson from the North region reported total sales of $1.2M, exceeding her target by 15%. The East region, managed by Robert Chen, achieved $950K in sales, which was 5% below target. Michael Patel from the West region reported $1.05M in sales, meeting exactly 100% of his target. The South region's representative, Lisa Garcia, was unable to attend, but her preliminary report indicates sales of approximately $880K, about 8% below the quarterly target."
}
]
},
{
"role": "assistant",
"content": [
{
"text": "```json"
}
]
}
],
inferenceConfig={
"temperature": 0.1,
"topP": .99,
"maxTokens": 512
}
)
print(json.dumps(response, indent=2))
aws bedrock-runtime converse \
--model-id "us.amazon.nova-pro-v1:0" \
--system '[
{
"text": "You are an expert data analyst who can extract structured information from unstructured text.\nFollow these instructions:\n1. Read and understand the unstructured text in ## Text ##\n2. Identify the key entities, attributes, and relationships in the text\n3. Create a well-organized JSON structure that captures this information in ## JSON ##\n4. Add a brief explanation of your JSON structure design choices in ## Explanation ##"
}
]' \
--messages '[
{
"role": "user",
"content": [
{
"text": "## Text ##\nThe quarterly sales meeting was held on March 15, 2025. Sarah Johnson from the North region reported total sales of $1.2M, exceeding her target by 15%. The East region, managed by Robert Chen, achieved $950K in sales, which was 5% below target. Michael Patel from the West region reported $1.05M in sales, meeting exactly 100% of his target. The South region'\''s representative, Lisa Garcia, was unable to attend, but her preliminary report indicates sales of approximately $880K, about 8% below the quarterly target."
}
]
}
]' \
--inference-config '{
"temperature": 0.1,
"topP": 0.99,
"maxTokens": 512
}' \
--region us-west-2
{
"modelId": "us.amazon.nova-pro-v1:0",
"system": [
{
"text": "You are an expert data analyst who can extract structured information from unstructured text.\nFollow these instructions:\n1. Read and understand the unstructured text in ## Text ##\n2. Identify the key entities, attributes, and relationships in the text\n3. Create a well-organized JSON structure that captures this information in ## JSON ##\n4. Add a brief explanation of your JSON structure design choices in ## Explanation ##"
}
],
"messages": [
{
"role": "user",
"content": [
{
"text": "## Text ##\nThe quarterly sales meeting was held on March 15, 2025. Sarah Johnson from the North region reported total sales of $1.2M, exceeding her target by 15%. The East region, managed by Robert Chen, achieved $950K in sales, which was 5% below target. Michael Patel from the West region reported $1.05M in sales, meeting exactly 100% of his target. The South region's representative, Lisa Garcia, was unable to attend, but her preliminary report indicates sales of approximately $880K, about 8% below the quarterly target."
}
]
}
],
"inferenceConfig": {
"temperature": 0.1,
"topP": 0.99,
"maxTokens": 512
}
}