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import boto3
GUARDRAIL_ID = "xxxxx"
GUARDRAIL_VERSION = "DRAFT"
REGION = "us-west-2"
bedrock = boto3.client("bedrock-runtime", region_name=REGION)
def apply_guardrail(text: str, source: str):
response = bedrock.apply_guardrail(
guardrailIdentifier=GUARDRAIL_ID,
guardrailVersion=GUARDRAIL_VERSION,
source=source,
content=[
{
"text": {
"text": text
}
}
]
)
return response
def classify(response):
"""用 API 原生值判断结果。
返回 (action, rule_actions):
action = 顶层 response["action"]: NONE / GUARDRAIL_INTERVENED
rule_actions= 命中的各规则的原生 action 集合,值来自 API:
BLOCKED -> 拒绝(拦截)
ANONYMIZED -> 修改(脱敏)
NONE -> 仅检测不动作
"""
action = response["action"]
rule_actions = {h["action"] for h in collect_hits(response) if h.get("action")}
return action, rule_actions
def collect_hits(response):
"""收集每条命中的规则,返回结构化列表 —— 覆盖 Guardrail 的全部策略类型。
每个元素: {policy, name, action, match, confidence?}
policy : 哪类策略 (topic/content/word/profanity/pii/regex/grounding)
name : 规则名 / 类型
action : 这条规则的动作 (BLOCKED / ANONYMIZED / NONE / ...)
match : 实际匹配到的文本(主题/内容过滤等没有则为 None)
覆盖说明:Guardrail 所有策略块都在这里处理,新增/未知的块也用
通用兜底逻辑捞出来,保证"漏不掉"(unknown policy 也会被记录)。
"""
BLOCK_MAP = {
"topicPolicy": [("topics", "topic", "name")],
"contentPolicy": [("filters", "content", "type")],
"wordPolicy": [("customWords", "word", "match"),
("managedWordLists", "profanity", "type")],
"sensitiveInformationPolicy":[("piiEntities", "pii", "type"),
("regexes", "regex", "name")],
"contextualGroundingPolicy": [("filters", "grounding", "type")],
}
hits = []
seen_blocks = set()
for a in response.get("assessments", []):
for block_name, sublists in BLOCK_MAP.items():
block = a.get(block_name)
if not block:
continue
seen_blocks.add(block_name)
for list_key, policy, name_field in sublists:
for item in block.get(list_key, []):
if not item.get("detected", True):
continue
hits.append({
"policy": policy,
"name": item.get(name_field),
"action": item.get("action"),
"match": item.get("match"),
"confidence": item.get("confidence"),
})
for k, v in a.items():
if k in ("invocationMetrics", "appliedGuardrailDetails"):
continue
if k not in BLOCK_MAP and isinstance(v, dict):
hits.append({"policy": f"unknown:{k}", "name": None,
"action": None, "match": None,
"raw": v})
return hits
def analyze(text, source="OUTPUT"):
"""对一段文本调 Guardrail,返回结构化的完整结果(便于统计/落库)。
字段全部用 API 原生值:
action = NONE / GUARDRAIL_INTERVENED
rule_actions = 命中各规则的原生动作集合: BLOCKED(拒绝) / ANONYMIZED(脱敏) / NONE
"""
resp = apply_guardrail(text, source)
action, rule_actions = classify(resp)
outs = resp.get("outputs", [])
return {
"input": text,
"source": source,
"action": action,
"actionReason": resp.get("actionReason"),
"rule_actions": sorted(rule_actions),
"returned_text": outs[0]["text"] if outs else None,
"hits": collect_hits(resp),
}
def show(text, source="OUTPUT"):
r = analyze(text, source)
print("#" * 60)
print(f"输入 : {r['input']} (source={r['source']})")
print(f"action : {r['action']}")
print(f"actionReason: {r['actionReason']}")
print(f"rule_actions: {r['rule_actions'] or '(无)'}")
print(f"返回内容 : {r['returned_text'] or '(无,原样放行)'}")
if r["hits"]:
print("命中规则 :")
for h in r["hits"]:
m = f" 匹配='{h['match']}'" if h.get("match") else ""
c = f" 置信度={h['confidence']}" if h.get("confidence") else ""
print(f" - [{h['policy']}] {h['name']} -> {h['action']}{m}{c}")
else:
print("命中规则 : (无)")
print()
if __name__ == "__main__":
show("You are a fucking asshole.")
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