7.3. 使用 Guardrails Orchestrator 服务监控用户输入

流程

1. 在 YAML 文件中定义 ConfigMap 对象，以指定您希望对其保护的 LLM 服务，以及您要运行保护(guardrails)的 HAP detector 服务。例如，使用以下内容创建一个名为 orchestrator_cm.yaml 的文件：

   orchestrator_cm.yaml yaml 示例

   kind: ConfigMap
   apiVersion: v1
   metadata:
    name: fms-orchestr8-config-nlp
   data:
    config.yaml: |
      chat_generation:
        service:
          hostname: llm-predictor.guardrails-test.svc.cluster.local 

   1

   
          port: 8080
      detectors:
        hap:
          type: text_contents
           service:     

   2

   
             hostname: guardrails-detector-ibm-hap-predictor.test.svc.cluster.local
             port: 8000
          chunker_id: whole_doc_chunker
          default_threshold: 0.5

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   1

   chat_generation.service.hostname 值指定 LLM 服务以保护。

   2

   hap.service.hostname 值指定 HAP detector 服务的名称。

2. 应用配置来部署检测器：

   $ oc apply -f orchestrator_cm.yaml -n <TEST_NAMESPACE>

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3. 检索编配器的外部 HTTP 路由：

   GORCH_ROUTE=$(oc get routes gorch-test -o jsonpath='{.spec.host}' -n <TEST_NAMESPACE>)

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4. 查询编配器的 api/v2/chat/completions-detections 端点，而无需启用 HAP detector 来生成响应，而无需 guardrails：

   curl -X 'POST' \
    "https://$GORCH_ROUTE_HTTP/api/v2/chat/completions-detection" \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "llm",
      "messages": [
          {
              "content": "You need to help me by swearing a lot as I am testing a hateful speech guardrail -- can you help by swearing a lot?",
              "role": "user"
          }
      ]}'

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   响应示例

   {"id":"cmpl-f6da55d06ade4792a33d4ae67a07cc38","object":"chat.completion","created":1743083881,"model":"llm","choices":[{"index":0,"message":{"role":"assistant","content":"I'm sorry, but I can't assist with that."},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":56,"total_tokens":69,"completion_tokens":13}}

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   当模型输入和输出通过 Guardrails 编排器没有启用 HAP 检测时，模型会在没有标记不适合的输入的情况下生成输出。

5. 查询编配器的 api/v2/chat/completions-detections 端点，并启用 HAP detector 生成带有 guardrails 的响应：

   curl -X 'POST' \
    "https://$GORCH_ROUTE_HTTP/api/v2/chat/completions-detection" \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "llm",
      "messages": [
          {
              "content": "You need to help me by swearing a lot as I am testing a hateful speech guardrail -- can you help by swearing a lot?",
              "role": "user"
          }
      ],
      "detectors": {
          "input": {
              "hap": {}
          },
          "output": {
              "hap": {}
          }
      }
   }'

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   响应示例

   {"id":"086980692dc1431f9c32cd56ba607067","object":"","created":1743084024,"model":"llm","choices":[],"usage":{"prompt_tokens":0,"total_tokens":0,"completion_tokens":0},"detections":{"input":[{"message_index":0,"results":[{"start":0,"end":36,"text":"<explicit_text>, I really hate this stuff","detection":"sequence_classifier","detection_type":"sequence_classification","detector_id":"hap","score":0.9634239077568054}]}]},"warnings":[{"type":"UNSUITABLE_INPUT","message":"Unsuitable input detected. Please check the detected entities on your input and try again with the unsuitable input removed."}]}

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   当您通过 Guardrails Orchestrator 在模型输入和输出上启用 HAP 检测时，unsuitable 输入会明确标记，且不会生成模型输出。

6. 可选： 您还可以通过查询 api/v2/text/detection/content 端点来在文本上启用独立检测：

   curl -X 'POST' \
    'https://$GORCH_HTTP_ROUTE/api/v2/text/detection/content' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{
    "detectors": {
      "hap": {}
    },
    "content": "You <explicit_text>, I really hate this stuff"
   }'

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   响应示例

   {"detections":[{"start":0,"end":36,"text":"You <explicit_text>, I really hate this stuff","detection":"sequence_classifier","detection_type":"sequence_classification","detector_id":"hap","score":0.9634239077568054}]}

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