bedrock for comments analysis

lambda_handler.py

import json
import base64
import boto3
import os

tools = [
    {
        "toolSpec": {
            "name": "analysis_product_review",
            "description": "Analysis the product reviews, determine its category, sentiment.",
            "inputSchema": {
                "json": {
                    "type": "object",
                    "properties": {
                        "category": {
                            "type": "string",
                            "description": "The category of the review",
                            "enum": ["Sizing", "Style", "Color", "Materials", "Others"]
                        },
                        "intention": {
                            "type": "string",
                            "description": "The intention of this review",
                            "enum": ["Praise", "Suggest", "Complain", "Spam", "Others"]
                        },
                        "tags": {
                            "type": "array",
                            "description": "An array of summaried tags of this review",
                            "items": {"type": "string"}
                        },
                        "sentiment": {
                            "type": "string",
                            "description": "The sentiment of the review",
                            "enum": ["Positive", "Neutral", "Negative"]
                        },
                        "confidence": {
                            "type": "number",
                            "description": "The confidence of the analysis, 0.0-1.0 (inclusive)",
                        }
                    },
                    "required": ["category", "sentiment", "intention", "tags", "confidence"]
                }
            }
        }
    }
]


def get_review_enrichment(br_runtime, o_review):

    prompt = """
    You are a helpful assistant that analyzes the product review and determine its category and sentiment.
  
    Use the analysis_product_review tool to provide the extracted information.

    Here's the original review message:
    """
    # https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html
    # https://community.aws/content/2hWA16FSt2bIzKs0Z1fgJBwu589/generating-json-with-the-amazon-bedrock-converse-api
    resp = br_runtime.converse(
        # change at your wish
        # anthropic.claude-3-5-sonnet-20241022-v2:0
        # anthropic.claude-3-haiku-20240307-v1:0
        # anthropic.claude-3-5-haiku-20241022-v1:0
        # us.meta.llama3-2-11b-instruct-v1:0, should use CRI
        # amazon.nova-pro-v1:0, us.amazon.nova-lite-v1:0
        modelId=os.environ.get(
            'MODEL_ID', 'us.amazon.nova-lite-v1:0'),
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "text": prompt,
                    },
                    {
                        "text": o_review,
                    }
                ]
            }
        ],
        inferenceConfig={
            "maxTokens": 4096,
            "temperature": 0
        },
        toolConfig={
            "tools": tools,
            # only Claude supports force use of the tool
            # "toolChoice": {
            #     "tool": {
            #         "name": "analysis_product_review"
            #     }
            # }
        }
    )
    for content in resp['output']['message']['content']:
        if content is not None and 'toolUse' in content.keys():
            return content['toolUse']['input']
    # no msg extracted
    return None


br_rt = boto3.client('bedrock-runtime', region_name='us-west-2')


def lambda_handler(event, context):
    b6 = base64.b64decode(event[0]['data']).decode('utf-8')
    payload = json.loads(b6)
    text = payload.get('text', '').strip()
    if text:
        review_info = get_review_enrichment(br_rt, text)
        if review_info is not None:
            payload.update(review_info)
    else:
        payload.update({"category": "unknown", "sentiment": "unknown"})
    return payload