tjdify/api/tests/integration_tests/model_runtime/replicate/test_llm.py

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, SystemPromptMessage, UserPromptMessage
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.replicate.llm.llm import ReplicateLargeLanguageModel


def test_validate_credentials():
    model = ReplicateLargeLanguageModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model="meta/llama-2-13b-chat",
            credentials={
                "replicate_api_token": "invalid_key",
                "model_version": "f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d",
            },
        )

    model.validate_credentials(
        model="meta/llama-2-13b-chat",
        credentials={
            "replicate_api_token": os.environ.get("REPLICATE_API_KEY"),
            "model_version": "f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d",
        },
    )


def test_invoke_model():
    model = ReplicateLargeLanguageModel()

    response = model.invoke(
        model="meta/llama-2-13b-chat",
        credentials={
            "replicate_api_token": os.environ.get("REPLICATE_API_KEY"),
            "model_version": "f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d",
        },
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Who are you?"),
        ],
        model_parameters={
            "temperature": 1.0,
            "top_k": 2,
            "top_p": 0.5,
        },
        stop=["How"],
        stream=False,
        user="abc-123",
    )

    assert isinstance(response, LLMResult)
    assert len(response.message.content) > 0


def test_invoke_stream_model():
    model = ReplicateLargeLanguageModel()

    response = model.invoke(
        model="mistralai/mixtral-8x7b-instruct-v0.1",
        credentials={
            "replicate_api_token": os.environ.get("REPLICATE_API_KEY"),
            "model_version": "2b56576fcfbe32fa0526897d8385dd3fb3d36ba6fd0dbe033c72886b81ade93e",
        },
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Who are you?"),
        ],
        model_parameters={
            "temperature": 1.0,
            "top_k": 2,
            "top_p": 0.5,
        },
        stop=["How"],
        stream=True,
        user="abc-123",
    )

    assert isinstance(response, Generator)

    for chunk in response:
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)


def test_get_num_tokens():
    model = ReplicateLargeLanguageModel()

    num_tokens = model.get_num_tokens(
        model="",
        credentials={
            "replicate_api_token": os.environ.get("REPLICATE_API_KEY"),
            "model_version": "2b56576fcfbe32fa0526897d8385dd3fb3d36ba6fd0dbe033c72886b81ade93e",
        },
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Hello World!"),
        ],
    )

    assert num_tokens == 14