DiDi의 Amazon Bedrock 상담 품질관리 시스템

import boto3
bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
MODEL_ID = ""

def build_output_schema(num_criteria):
    """Derive the JSON schema from the checklist size — adding a new compliance item requires only a config change, not a code change."""
    criteria = {
        f"criteria_{i}": {
            "type": "object",
            "properties": {
                "reasoning": {"type": "string"},
                "score": {"type": "integer"},  # 0 = pass, 1 = fail
            },
            "required": ["reasoning", "score"],
        }
        for i in range(1, num_criteria + 1)
    }
    return {
        "type": "object",
        "properties": {"scoring": {"type": "object", "properties": criteria}},
        "required": ["scoring"],
    }

def assemble_prompt(criteria_config, language, business_line):
    """Dynamic assembly: one template covers every language x business-line combination. Context lives in external config, injected at call time."""
    criteria_block = "
".join(
        f"CRITERIA_{i}:
- {c['parameter']}
- PASS: {c['pass']}
- FAIL: {c['fail']}"
        for i, c in enumerate(criteria_config, 1)
    )
    return (
        f"You are a customer service quality auditor for DiDi.
"
        f"LANGUAGE CONTEXT: the conversation is in {language}.
"
        f"BUSINESS CONTEXT: this is a {business_line} service interaction.

"
        f"SCORING CRITERIA TO EVALUATE:
{criteria_block}"
    )

def evaluate(transcript, criteria_config, language, business_line):
    system_prompt = assemble_prompt(criteria_config, language, business_line)
    schema = build_output_schema(len(criteria_config))
    response = bedrock.converse(
        modelId=MODEL_ID,
        system=[{"text": system_prompt}],
        messages=[{"role": "user", "content": [{"text": transcript}]}],
        # Tool Use with a forced tool choice constrains the model to emit
        # schema-valid JSON — every score carries its own reasoning chain.
        toolConfig={
            "tools": [{
                "toolSpec": {
                    "name": "output_result",
                    "description": "Output the structured evaluation result",
                    "inputSchema": {"json": schema},
                }
            }],
            "toolChoice": {"tool": {"name": "output_result"}},
        },
        inferenceConfig={"maxTokens": 8192, "temperature": 0.0},
    )
    for block in response["output"]["message"]["content"]:
        if "toolUse" in block:
            return block["toolUse"]["input"]