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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
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