Update requirement_check/README.md

requirement_check/README.md

@@ -34,9 +34,52 @@ This **Requirement Checker** family of adapters are designed to check if specifi
 
 
 ### Quickstart Example
-
-
-
+First, see information elsewhere in this repo on how to start up a vLLM server hosting the LoRAs and/or aLoRAs. Once this server is started, it can be queried via the OpenAI API. An example for this intrinsic follows. 
+
+```
+import os
+import openai
+import json
+import granite_common
+
+PROMPT = "What is IBM?"
+REQUIREMENTS = "Use a formal tone.\n Do not use long words."
+REPONSE = ... # this should be generated by the base model corresponding to the chosen adapter
+REQUIREMENT_TO_CHECK = "Use a formal tone."
+
+request = {
+  "messages": [
+    {
+      "content": PROMPT + "\nRequirements: " + REQUIREMENTS,
+      "role": "user"
+    },
+    {
+      "role": "assistant",
+      "content": RESPONSE
+    },
+  ],
+  "extra_body": {
+    "requirement": REQUIREMENT_TO_CHECK
+  },
+  "model": "requirement_check",
+  "temperature": 0.0
+}
+openai_base_url = ...
+openai_api_key = ...
+io_yaml_file = "./rag_intrinsics_lib/requirement_check/.../io.yaml"
+
+rewriter = granite_common.IntrinsicsRewriter(config_file=io_yaml_file)
+result_processor = granite_common.IntrinsicsResultProcessor(config_file=io_yaml_file)
+
+rewritten_request = rewriter.transform(request)
+
+client = openai.OpenAI(base_url=openai_base_url, api_key=openai_api_key)
+chat_completion = client.chat.completions.create(**rewritten_request.model_dump())
+
+transformed_completion = result_processor.transform(chat_completion)
+
+print(transformed_completion.model_dump_json(indent=2))
+```
 
 ## Evaluation