feat: Add Contract Tests for new Gen AI attributes for foundational m…

…odels (#292)

contract tests for new gen_ai inference parameters added in 


#290

<img width="1563" alt="image"
src="https://github.com/user-attachments/assets/3ea5979d-43b2-43d6-8730-708855969d8a">

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choice.

---------

Co-authored-by: Michael He <[email protected]>

contract-tests/images/applications/botocore/botocore_server.py

@@ -6,13 +6,15 @@
 import tempfile
 from collections import namedtuple
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from io import BytesIO
 from threading import Thread
 
 import boto3
 import requests
 from botocore.client import BaseClient
 from botocore.config import Config
 from botocore.exceptions import ClientError
+from botocore.response import StreamingBody
 from typing_extensions import Tuple, override
 
 _PORT: int = 8080
@@ -285,28 +287,22 @@ def _handle_bedrock_request(self) -> None:
                 },
             )
         elif self.in_path("invokemodel/invoke-model"):
+            model_id, request_body, response_body = get_model_request_response(self.path)
+
             set_main_status(200)
             bedrock_runtime_client.meta.events.register(
                 "before-call.bedrock-runtime.InvokeModel",
-                inject_200_success,
-            )
-            model_id = "amazon.titan-text-premier-v1:0"
-            user_message = "Describe the purpose of a 'hello world' program in one line."
-            prompt = f"<s>[INST] {user_message} [/INST]"
-            body = json.dumps(
-                {
-                    "inputText": prompt,
-                    "textGenerationConfig": {
-                        "maxTokenCount": 3072,
-                        "stopSequences": [],
-                        "temperature": 0.7,
-                        "topP": 0.9,
-                    },
-                }
+                lambda **kwargs: inject_200_success(
+                    modelId=model_id,
+                    body=response_body,
+                    **kwargs,
+                ),
             )
             accept = "application/json"
             content_type = "application/json"
-            bedrock_runtime_client.invoke_model(body=body, modelId=model_id, accept=accept, contentType=content_type)
+            bedrock_runtime_client.invoke_model(
+                body=request_body, modelId=model_id, accept=accept, contentType=content_type
+            )
         else:
             set_main_status(404)
 
@@ -378,6 +374,137 @@ def _end_request(self, status_code: int):
         self.end_headers()
 
 
+def get_model_request_response(path):
+    prompt = "Describe the purpose of a 'hello world' program in one line."
+    model_id = ""
+    request_body = {}
+    response_body = {}
+
+    if "amazon.titan" in path:
+        model_id = "amazon.titan-text-premier-v1:0"
+
+        request_body = {
+            "inputText": prompt,
+            "textGenerationConfig": {
+                "maxTokenCount": 3072,
+                "stopSequences": [],
+                "temperature": 0.7,
+                "topP": 0.9,
+            },
+        }
+
+        response_body = {
+            "inputTextTokenCount": 15,
+            "results": [
+                {
+                    "tokenCount": 13,
+                    "outputText": "text-test-response",
+                    "completionReason": "CONTENT_FILTERED",
+                },
+            ],
+        }
+
+    if "anthropic.claude" in path:
+        model_id = "anthropic.claude-v2:1"
+
+        request_body = {
+            "anthropic_version": "bedrock-2023-05-31",
+            "max_tokens": 1000,
+            "temperature": 0.99,
+            "top_p": 1,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [{"type": "text", "text": prompt}],
+                },
+            ],
+        }
+
+        response_body = {
+            "stop_reason": "end_turn",
+            "usage": {
+                "input_tokens": 15,
+                "output_tokens": 13,
+            },
+        }
+
+    if "meta.llama" in path:
+        model_id = "meta.llama2-13b-chat-v1"
+
+        request_body = {"prompt": prompt, "max_gen_len": 512, "temperature": 0.5, "top_p": 0.9}
+
+        response_body = {"prompt_token_count": 31, "generation_token_count": 49, "stop_reason": "stop"}
+
+    if "cohere.command" in path:
+        model_id = "cohere.command-r-v1:0"
+
+        request_body = {
+            "chat_history": [],
+            "message": prompt,
+            "max_tokens": 512,
+            "temperature": 0.5,
+            "p": 0.65,
+        }
+
+        response_body = {
+            "chat_history": [
+                {"role": "USER", "message": prompt},
+                {"role": "CHATBOT", "message": "test-text-output"},
+            ],
+            "finish_reason": "COMPLETE",
+            "text": "test-generation-text",
+        }
+
+    if "ai21.jamba" in path:
+        model_id = "ai21.jamba-1-5-large-v1:0"
+
+        request_body = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": prompt,
+                },
+            ],
+            "top_p": 0.8,
+            "temperature": 0.6,
+            "max_tokens": 512,
+        }
+
+        response_body = {
+            "stop_reason": "end_turn",
+            "usage": {
+                "prompt_tokens": 21,
+                "completion_tokens": 24,
+            },
+            "choices": [
+                {"finish_reason": "stop"},
+            ],
+        }
+
+    if "mistral" in path:
+        model_id = "mistral.mistral-7b-instruct-v0:2"
+
+        request_body = {
+            "prompt": prompt,
+            "max_tokens": 4096,
+            "temperature": 0.75,
+            "top_p": 0.99,
+        }
+
+        response_body = {
+            "outputs": [
+                {
+                    "text": "test-output-text",
+                    "stop_reason": "stop",
+                },
+            ]
+        }
+
+    json_bytes = json.dumps(response_body).encode("utf-8")
+
+    return model_id, json.dumps(request_body), StreamingBody(BytesIO(json_bytes), len(json_bytes))
+
+
 def set_main_status(status: int) -> None:
     RequestHandler.main_status = status
 
@@ -490,11 +617,16 @@ def inject_200_success(**kwargs):
     guardrail_arn = kwargs.get("guardrailArn")
     if guardrail_arn is not None:
         response_body["guardrailArn"] = guardrail_arn
+    model_id = kwargs.get("modelId")
+    if model_id is not None:
+        response_body["modelId"] = model_id
 
     HTTPResponse = namedtuple("HTTPResponse", ["status_code", "headers", "body"])
     headers = kwargs.get("headers", {})
     body = kwargs.get("body", "")
+    response_body["body"] = body
     http_response = HTTPResponse(200, headers=headers, body=body)
+
     return http_response, response_body
 
 

contract-tests/tests/test/amazon/base/contract_test_base.py

@@ -173,6 +173,12 @@ def _assert_int_attribute(self, attributes_dict: Dict[str, AnyValue], key: str,
         self.assertIsNotNone(actual_value)
         self.assertEqual(expected_value, actual_value.int_value)
 
+    def _assert_float_attribute(self, attributes_dict: Dict[str, AnyValue], key: str, expected_value: float) -> None:
+        self.assertIn(key, attributes_dict)
+        actual_value: AnyValue = attributes_dict[key]
+        self.assertIsNotNone(actual_value)
+        self.assertEqual(expected_value, actual_value.double_value)
+
     def _assert_match_attribute(self, attributes_dict: Dict[str, AnyValue], key: str, pattern: str) -> None:
         self.assertIn(key, attributes_dict)
         actual_value: AnyValue = attributes_dict[key]
@@ -237,5 +243,5 @@ def _is_valid_regex(self, pattern: str) -> bool:
         try:
             re.compile(pattern)
             return True
-        except re.error:
+        except (re.error, StopIteration, RuntimeError, KeyError):
             return False