add unittest of xAI

api/core/model_runtime/model_providers/x/llm/llm.py

@@ -23,6 +23,7 @@ def _invoke(
         stream: bool = True,
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials)
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -31,5 +32,5 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
 
     @staticmethod
     def _add_custom_parameters(credentials) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["api_base"]) / "v1")
+        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1")
         credentials["mode"] = LLMMode.CHAT.value

api/core/model_runtime/model_providers/x/x.py

@@ -22,4 +22,4 @@ def validate_provider_credentials(self, credentials: dict) -> None:
             raise ex
         except Exception as ex:
             logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
-            raise ex
+            raise ex

api/core/model_runtime/model_providers/x/x.yaml

@@ -27,7 +27,7 @@ provider_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
-    - variable: api_base
+    - variable: endpoint_url
       label:
         en_US: API Base
       type: text-input

api/tests/integration_tests/.env.example

@@ -95,3 +95,7 @@ GPUSTACK_API_KEY=
 
 # Gitee AI Credentials
 GITEE_AI_API_KEY=
+
+# xAI Credentials
+XAI_API_KEY=
+XAI_API_BASE=

api/tests/integration_tests/model_runtime/x/test_llm.py

@@ -0,0 +1,204 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessageTool,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.x.llm.llm import XAILargeLanguageModel
+
+"""FOR MOCK FIXTURES, DO NOT REMOVE"""
+from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
+
+
+def test_predefined_models():
+    model = XAILargeLanguageModel()
+    model_schemas = model.predefined_models()
+
+    assert len(model_schemas) >= 1
+    assert isinstance(model_schemas[0], AIModelEntity)
+
+
+@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_validate_credentials_for_chat_model(setup_openai_mock):
+    model = XAILargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        # model name to gpt-3.5-turbo because of mocking
+        model.validate_credentials(
+            model="gpt-3.5-turbo",
+            credentials={"api_key": "invalid_key", "endpoint_url": os.environ.get("XAI_API_BASE"), "mode": "chat"},
+        )
+
+    model.validate_credentials(
+        model="grok-beta",
+        credentials={
+            "api_key": os.environ.get("XAI_API_KEY"),
+            "endpoint_url": os.environ.get("XAI_API_BASE"),
+            "mode": "chat",
+        },
+    )
+
+
+@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_invoke_chat_model(setup_openai_mock):
+    model = XAILargeLanguageModel()
+
+    result = model.invoke(
+        model="grok-beta",
+        credentials={
+            "api_key": os.environ.get("XAI_API_KEY"),
+            "endpoint_url": os.environ.get("XAI_API_BASE"),
+            "mode": "chat",
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "presence_penalty": 0.0,
+            "frequency_penalty": 0.0,
+            "max_tokens": 10,
+        },
+        stop=["How"],
+        stream=False,
+        user="foo",
+    )
+
+    assert isinstance(result, LLMResult)
+    assert len(result.message.content) > 0
+
+
+@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_invoke_chat_model_with_tools(setup_openai_mock):
+    model = XAILargeLanguageModel()
+
+    result = model.invoke(
+        model="grok-beta",
+        credentials={
+            "api_key": os.environ.get("XAI_API_KEY"),
+            "endpoint_url": os.environ.get("XAI_API_BASE"),
+            "mode": "chat",
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(
+                content="what's the weather today in London?",
+            ),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        tools=[
+            PromptMessageTool(
+                name="get_weather",
+                description="Determine weather in my location",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
+                        "unit": {"type": "string", "enum": ["c", "f"]},
+                    },
+                    "required": ["location"],
+                },
+            ),
+            PromptMessageTool(
+                name="get_stock_price",
+                description="Get the current stock price",
+                parameters={
+                    "type": "object",
+                    "properties": {"symbol": {"type": "string", "description": "The stock symbol"}},
+                    "required": ["symbol"],
+                },
+            ),
+        ],
+        stream=False,
+        user="foo",
+    )
+
+    assert isinstance(result, LLMResult)
+    assert isinstance(result.message, AssistantPromptMessage)
+
+
+@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_invoke_stream_chat_model(setup_openai_mock):
+    model = XAILargeLanguageModel()
+
+    result = model.invoke(
+        model="grok-beta",
+        credentials={
+            "api_key": os.environ.get("XAI_API_KEY"),
+            "endpoint_url": os.environ.get("XAI_API_BASE"),
+            "mode": "chat",
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        stream=True,
+        user="foo",
+    )
+
+    assert isinstance(result, Generator)
+
+    for chunk in result:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
+        if chunk.delta.finish_reason is not None:
+            assert chunk.delta.usage is not None
+            assert chunk.delta.usage.completion_tokens > 0
+
+
+def test_get_num_tokens():
+    model = XAILargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model="grok-beta",
+        credentials={"api_key": os.environ.get("XAI_API_KEY"), "endpoint_url": os.environ.get("XAI_API_BASE")},
+        prompt_messages=[UserPromptMessage(content="Hello World!")],
+    )
+
+    assert num_tokens == 10
+
+    num_tokens = model.get_num_tokens(
+        model="grok-beta",
+        credentials={"api_key": os.environ.get("XAI_API_KEY"), "endpoint_url": os.environ.get("XAI_API_BASE")},
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        tools=[
+            PromptMessageTool(
+                name="get_weather",
+                description="Determine weather in my location",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
+                        "unit": {"type": "string", "enum": ["c", "f"]},
+                    },
+                    "required": ["location"],
+                },
+            ),
+        ],
+    )
+
+    assert num_tokens == 77