diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb index 551e1c8df0..0443dbf844 100644 --- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "tags": [] }, @@ -44,10 +44,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" + "^C\n", + "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], @@ -57,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -76,7 +75,7 @@ "AIMessage(content=\" J'aime la programmation.\")" ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -101,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -110,7 +109,7 @@ "AIMessage(content=' プログラミングが大好きです')" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -154,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, @@ -165,27 +164,51 @@ "text": [ " ```python\n", "def is_prime(n):\n", - " if n <= 1:\n", - " return False\n", - " for i in range(2, n):\n", - " if n % i == 0:\n", - " return False\n", - " return True\n", + " \"\"\"\n", + " Check if a number is prime.\n", + "\n", + " Args:\n", + " n: The number to check.\n", + "\n", + " Returns:\n", + " True if n is prime, False otherwise.\n", + " \"\"\"\n", + "\n", + " # If n is 1, it is not prime.\n", + " if n == 1:\n", + " return False\n", + "\n", + " # Iterate over all numbers from 2 to the square root of n.\n", + " for i in range(2, int(n ** 0.5) + 1):\n", + " # If n is divisible by any number from 2 to its square root, it is not prime.\n", + " if n % i == 0:\n", + " return False\n", + "\n", + " # If n is divisible by no number from 2 to its square root, it is prime.\n", + " return True\n", + "\n", "\n", "def find_prime_numbers(n):\n", - " prime_numbers = []\n", - " for i in range(2, n + 1):\n", - " if is_prime(i):\n", - " prime_numbers.append(i)\n", - " return prime_numbers\n", + " \"\"\"\n", + " Find all prime numbers up to a given number.\n", + "\n", + " Args:\n", + " n: The upper bound for the prime numbers to find.\n", + "\n", + " Returns:\n", + " A list of all prime numbers up to n.\n", + " \"\"\"\n", "\n", - "print(find_prime_numbers(100))\n", - "```\n", + " # Create a list of all numbers from 2 to n.\n", + " numbers = list(range(2, n + 1))\n", "\n", - "Output:\n", + " # Iterate over the list of numbers and remove any that are not prime.\n", + " for number in numbers:\n", + " if not is_prime(number):\n", + " numbers.remove(number)\n", "\n", - "```\n", - "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n", + " # Return the list of prime numbers.\n", + " return numbers\n", "```\n" ] } @@ -199,6 +222,102 @@ "print(message.content)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Full generation info\n", + "\n", + "We can use the `generate` method to get back extra metadata like [safety attributes](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_confidence_scoring) and not just chat completions\n", + "\n", + "Note that the `generation_info` will be different depending if you're using a gemini model or not.\n", + "\n", + "### Gemini model\n", + "\n", + "`generation_info` will include:\n", + "\n", + "- `is_blocked`: whether generation was blocked or not\n", + "- `safety_ratings`: safety ratings' categories and probability labels" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'is_blocked': False,\n", + " 'safety_ratings': [{'category': 'HARM_CATEGORY_HARASSMENT',\n", + " 'probability_label': 'NEGLIGIBLE'},\n", + " {'category': 'HARM_CATEGORY_HATE_SPEECH',\n", + " 'probability_label': 'NEGLIGIBLE'},\n", + " {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n", + " 'probability_label': 'NEGLIGIBLE'},\n", + " {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n", + " 'probability_label': 'NEGLIGIBLE'}]}\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "\n", + "from langchain_core.messages import HumanMessage\n", + "from langchain_google_vertexai import ChatVertexAI, HarmBlockThreshold, HarmCategory\n", + "\n", + "human = \"Translate this sentence from English to French. I love programming.\"\n", + "messages = [HumanMessage(content=human)]\n", + "\n", + "\n", + "chat = ChatVertexAI(\n", + " model_name=\"gemini-pro\",\n", + " safety_settings={\n", + " HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE\n", + " },\n", + ")\n", + "\n", + "result = chat.generate([messages])\n", + "pprint(result.generations[0][0].generation_info)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Non-gemini model\n", + "\n", + "`generation_info` will include:\n", + "\n", + "- `is_blocked`: whether generation was blocked or not\n", + "- `safety_attributes`: a dictionary mapping safety attributes to their scores" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'is_blocked': False,\n", + " 'safety_attributes': {'Derogatory': 0.1,\n", + " 'Finance': 0.3,\n", + " 'Insult': 0.1,\n", + " 'Sexual': 0.1}}\n" + ] + } + ], + "source": [ + "chat = ChatVertexAI() # default is `chat-bison`\n", + "\n", + "result = chat.generate([messages])\n", + "pprint(result.generations[0][0].generation_info)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -210,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -224,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -268,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py b/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py index 391a7c7b1d..ba97adf52e 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py @@ -1,5 +1,13 @@ +from langchain_google_vertexai._enums import HarmBlockThreshold, HarmCategory from langchain_google_vertexai.chat_models import ChatVertexAI from langchain_google_vertexai.embeddings import VertexAIEmbeddings from langchain_google_vertexai.llms import VertexAI, VertexAIModelGarden -__all__ = ["ChatVertexAI", "VertexAIEmbeddings", "VertexAI", "VertexAIModelGarden"] +__all__ = [ + "ChatVertexAI", + "VertexAIEmbeddings", + "VertexAI", + "VertexAIModelGarden", + "HarmBlockThreshold", + "HarmCategory", +] diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py b/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py new file mode 100644 index 0000000000..00a2abaa32 --- /dev/null +++ b/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py @@ -0,0 +1,6 @@ +from vertexai.preview.generative_models import ( # type: ignore + HarmBlockThreshold, + HarmCategory, +) + +__all__ = ["HarmBlockThreshold", "HarmCategory"] diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py index 6dcc7a2d73..340acc05d8 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py @@ -1,6 +1,6 @@ """Utilities to init Vertex AI.""" from importlib import metadata -from typing import Any, Callable, Optional, Union +from typing import Any, Callable, Dict, Optional, Union import google.api_core from google.api_core.gapic_v1.client_info import ClientInfo @@ -86,3 +86,29 @@ def is_codey_model(model_name: str) -> bool: def is_gemini_model(model_name: str) -> bool: """Returns True if the model name is a Gemini model.""" return model_name is not None and "gemini" in model_name + + +def get_generation_info(candidate: Any, is_gemini: bool) -> Optional[Dict[str, Any]]: + try: + if is_gemini: + # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini#response_body + return { + "is_blocked": any( + [rating.blocked for rating in candidate.safety_ratings] + ), + "safety_ratings": [ + { + "category": rating.category.name, + "probability_label": rating.probability.name, + } + for rating in candidate.safety_ratings + ], + } + else: + # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-chat#response_body + return { + "is_blocked": candidate.is_blocked, + "safety_attributes": candidate.safety_attributes, + } + except Exception: + return None diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py index 72f23815d7..a76ade2d22 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py @@ -47,6 +47,9 @@ from vertexai.preview.generative_models import ( # type: ignore ) from langchain_google_vertexai._utils import ( + get_generation_info, + is_codey_model, + is_gemini_model, load_image_from_gcs, ) from langchain_google_vertexai.functions_utils import ( @@ -54,8 +57,6 @@ from langchain_google_vertexai.functions_utils import ( ) from langchain_google_vertexai.llms import ( _VertexAICommon, - is_codey_model, - is_gemini_model, ) logger = logging.getLogger(__name__) @@ -271,9 +272,16 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): def validate_environment(cls, values: Dict) -> Dict: """Validate that the python package exists in environment.""" is_gemini = is_gemini_model(values["model_name"]) + safety_settings = values["safety_settings"] + + if safety_settings and not is_gemini: + raise ValueError("Safety settings are only supported for Gemini models") + cls._init_vertexai(values) if is_gemini: - values["client"] = GenerativeModel(model_name=values["model_name"]) + values["client"] = GenerativeModel( + model_name=values["model_name"], safety_settings=safety_settings + ) else: if is_codey_model(values["model_name"]): model_cls = CodeChatModel @@ -306,6 +314,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): ValueError: if the last message in the list is not from human. """ should_stream = stream if stream is not None else self.streaming + safety_settings = kwargs.pop("safety_settings", None) if should_stream: stream_iter = self._stream( messages, stop=stop, run_manager=run_manager, **kwargs @@ -325,9 +334,17 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): # set param to `functions` until core tool/function calling implemented raw_tools = params.pop("functions") if "functions" in params else None tools = _format_tools_to_vertex_tool(raw_tools) if raw_tools else None - response = chat.send_message(message, generation_config=params, tools=tools) + response = chat.send_message( + message, + generation_config=params, + tools=tools, + safety_settings=safety_settings, + ) generations = [ - ChatGeneration(message=_parse_response_candidate(c)) + ChatGeneration( + message=_parse_response_candidate(c), + generation_info=get_generation_info(c, self._is_gemini_model), + ) for c in response.candidates ] else: @@ -339,7 +356,10 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): chat = self._start_chat(history, **params) response = chat.send_message(question.content, **msg_params) generations = [ - ChatGeneration(message=AIMessage(content=r.text)) + ChatGeneration( + message=AIMessage(content=r.text), + generation_info=get_generation_info(r, self._is_gemini_model), + ) for r in response.candidates ] return ChatResult(generations=generations) @@ -370,6 +390,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): logger.warning("ChatVertexAI does not currently support async streaming.") params = self._prepare_params(stop=stop, **kwargs) + safety_settings = kwargs.pop("safety_settings", None) msg_params = {} if "candidate_count" in params: msg_params["candidate_count"] = params.pop("candidate_count") @@ -382,22 +403,31 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): raw_tools = params.pop("functions") if "functions" in params else None tools = _format_tools_to_vertex_tool(raw_tools) if raw_tools else None response = await chat.send_message_async( - message, generation_config=params, tools=tools + message, + generation_config=params, + tools=tools, + safety_settings=safety_settings, ) generations = [ - ChatGeneration(message=_parse_response_candidate(c)) + ChatGeneration( + message=_parse_response_candidate(c), + generation_info=get_generation_info(c, self._is_gemini_model), + ) for c in response.candidates ] else: question = _get_question(messages) history = _parse_chat_history(messages[:-1]) - examples = kwargs.get("examples", None) + examples = kwargs.get("examples", None) or self.examples if examples: params["examples"] = _parse_examples(examples) chat = self._start_chat(history, **params) response = await chat.send_message_async(question.content, **msg_params) generations = [ - ChatGeneration(message=AIMessage(content=r.text)) + ChatGeneration( + message=AIMessage(content=r.text), + generation_info=get_generation_info(r, self._is_gemini_model), + ) for r in response.candidates ] return ChatResult(generations=generations) @@ -441,7 +471,10 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): for response in responses: if run_manager: run_manager.on_llm_new_token(response.text) - yield ChatGenerationChunk(message=AIMessageChunk(content=response.text)) + yield ChatGenerationChunk( + message=AIMessageChunk(content=response.text), + generation_info=get_generation_info(response, self._is_gemini_model), + ) def _start_chat( self, history: _ChatHistory, **kwargs: Any diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py index 6e02ae43f9..a71e6a0736 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py @@ -26,7 +26,10 @@ from vertexai.language_models import ( # type: ignore from vertexai.language_models._language_models import ( # type: ignore TextGenerationResponse, ) -from vertexai.preview.generative_models import GenerativeModel, Image # type: ignore +from vertexai.preview.generative_models import ( # type: ignore + GenerativeModel, + Image, +) from vertexai.preview.language_models import ( # type: ignore CodeGenerationModel as PreviewCodeGenerationModel, ) @@ -34,9 +37,11 @@ from vertexai.preview.language_models import ( TextGenerationModel as PreviewTextGenerationModel, ) +from langchain_google_vertexai._enums import HarmBlockThreshold, HarmCategory from langchain_google_vertexai._utils import ( create_retry_decorator, get_client_info, + get_generation_info, is_codey_model, is_gemini_model, ) @@ -66,7 +71,10 @@ def _completion_with_retry( ) -> Any: if is_gemini: return llm.client.generate_content( - prompt, stream=stream, generation_config=kwargs + prompt, + stream=stream, + safety_settings=kwargs.pop("safety_settings", None), + generation_config=kwargs, ) else: if stream: @@ -94,7 +102,9 @@ async def _acompletion_with_retry( ) -> Any: if is_gemini: return await llm.client.generate_content_async( - prompt, generation_config=kwargs + prompt, + generation_config=kwargs, + safety_settings=kwargs.pop("safety_settings", None), ) return await llm.client.predict_async(prompt, **kwargs) @@ -141,6 +151,21 @@ class _VertexAICommon(_VertexAIBase): """How many completions to generate for each prompt.""" streaming: bool = False """Whether to stream the results or not.""" + safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None + """The default safety settings to use for all generations. + + For example: + + from langchain_google_vertexai import HarmBlockThreshold, HarmCategory + + safety_settings = { + HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, + } + """ # noqa: E501 @property def _llm_type(self) -> str: @@ -237,9 +262,13 @@ class VertexAI(_VertexAICommon, BaseLLM): """Validate that the python package exists in environment.""" tuned_model_name = values.get("tuned_model_name") model_name = values["model_name"] + safety_settings = values["safety_settings"] is_gemini = is_gemini_model(values["model_name"]) cls._init_vertexai(values) + if safety_settings and (not is_gemini or tuned_model_name): + raise ValueError("Safety settings are only supported for Gemini models") + if is_codey_model(model_name): model_cls = CodeGenerationModel preview_model_cls = PreviewCodeGenerationModel @@ -257,8 +286,12 @@ class VertexAI(_VertexAICommon, BaseLLM): ) else: if is_gemini: - values["client"] = model_cls(model_name=model_name) - values["client_preview"] = preview_model_cls(model_name=model_name) + values["client"] = model_cls( + model_name=model_name, safety_settings=safety_settings + ) + values["client_preview"] = preview_model_cls( + model_name=model_name, safety_settings=safety_settings + ) else: values["client"] = model_cls.from_pretrained(model_name) values["client_preview"] = preview_model_cls.from_pretrained(model_name) @@ -285,14 +318,14 @@ class VertexAI(_VertexAICommon, BaseLLM): self, response: TextGenerationResponse ) -> GenerationChunk: """Converts a stream response to a generation chunk.""" - try: - generation_info = { - "is_blocked": response.is_blocked, - "safety_attributes": response.safety_attributes, - } - except Exception: - generation_info = None - return GenerationChunk(text=response.text, generation_info=generation_info) + generation_info = get_generation_info(response, self._is_gemini_model) + + return GenerationChunk( + text=response.text + if hasattr(response, "text") + else "", # might not exist if blocked + generation_info=generation_info, + ) def _generate( self, diff --git a/libs/partners/google-vertexai/poetry.lock b/libs/partners/google-vertexai/poetry.lock index 4c55611ca7..48233dce17 100644 --- a/libs/partners/google-vertexai/poetry.lock +++ b/libs/partners/google-vertexai/poetry.lock @@ -504,13 +504,13 @@ uritemplate = ">=3.0.1,<5" [[package]] name = "google-auth" -version = "2.26.1" +version = "2.26.2" description = "Google Authentication Library" optional = false python-versions = ">=3.7" files = [ - 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assert response.generations[0][0] == sync_response.generations[0][0] + sync_generation = cast(ChatGeneration, sync_response.generations[0][0]) + async_generation = cast(ChatGeneration, response.generations[0][0]) + + # assert some properties to make debugging easier + assert sync_generation.message.content == async_generation.message.content + assert sync_generation.generation_info == async_generation.generation_info + assert sync_generation == async_generation @pytest.mark.parametrize("model_name", ["chat-bison@001", "gemini-pro"]) diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_llms.py b/libs/partners/google-vertexai/tests/integration_tests/test_llms.py index 14f84c0616..823c8671dc 100644 --- a/libs/partners/google-vertexai/tests/integration_tests/test_llms.py +++ b/libs/partners/google-vertexai/tests/integration_tests/test_llms.py @@ -42,6 +42,7 @@ def test_vertex_call(model_name: str) -> None: assert isinstance(output, str) +@pytest.mark.xfail(reason="VertexAI doesn't always respect number of candidates") def test_vertex_generate() -> None: llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001") output = llm.generate(["Say foo:"]) @@ -50,6 +51,7 @@ def test_vertex_generate() -> None: assert len(output.generations[0]) == 2 +@pytest.mark.xfail(reason="VertexAI doesn't always respect number of candidates") def test_vertex_generate_code() -> None: llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001") output = llm.generate(["generate a python method that says foo:"]) @@ -87,6 +89,7 @@ async def test_vertex_consistency() -> None: assert output.generations[0][0].text == async_output.generations[0][0].text +@pytest.mark.skip("CI testing not set up") @pytest.mark.parametrize( "endpoint_os_variable_name,result_arg", [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)], @@ -115,6 +118,7 @@ def test_model_garden( assert llm._llm_type == "vertexai_model_garden" +@pytest.mark.skip("CI testing not set up") @pytest.mark.parametrize( "endpoint_os_variable_name,result_arg", [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)], @@ -143,6 +147,7 @@ def test_model_garden_generate( assert len(output.generations) == 2 +@pytest.mark.skip("CI testing not set up") @pytest.mark.asyncio @pytest.mark.parametrize( "endpoint_os_variable_name,result_arg", diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_llms_safety.py b/libs/partners/google-vertexai/tests/integration_tests/test_llms_safety.py new file mode 100644 index 0000000000..7e526cbf27 --- /dev/null +++ b/libs/partners/google-vertexai/tests/integration_tests/test_llms_safety.py @@ -0,0 +1,97 @@ +from langchain_core.outputs import LLMResult + +from langchain_google_vertexai import HarmBlockThreshold, HarmCategory, VertexAI + +SAFETY_SETTINGS = { + HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, +} + + +# below context and question are taken from one of opensource QA datasets +BLOCKED_PROMPT = """ +You are agent designed to answer questions. +You are given context in triple backticks. +``` +The religion\'s failure to report abuse allegations to authorities has also been +criticized. The Watch Tower Society\'s policy is that elders inform authorities when + required by law to do so, but otherwise leave that action up to the victim and his + or her family. The Australian Royal Commission into Institutional Responses to Child +Sexual Abuse found that of 1006 alleged perpetrators of child sexual abuse +identified by the Jehovah\'s Witnesses within their organization since 1950, +"not one was reported by the church to secular authorities." William Bowen, a former +Jehovah\'s Witness elder who established the Silentlambs organization to assist sex +abuse victims within the religion, has claimed Witness leaders discourage followers +from reporting incidents of sexual misconduct to authorities, and other critics claim +the organization is reluctant to alert authorities in order to protect its "crime-free" + reputation. In court cases in the United Kingdom and the United States the Watch Tower + Society has been found to have been negligent in its failure to protect children from + known sex offenders within the congregation and the Society has settled other child +abuse lawsuits out of court, reportedly paying as much as $780,000 to one plaintiff +without admitting wrongdoing. +``` +Question: What have courts in both the UK and the US found the Watch Tower Society to + have been for failing to protect children from sexual predators within the + congregation ? +Answer: +""" + + +def test_gemini_safety_settings_generate() -> None: + llm = VertexAI(model_name="gemini-pro", safety_settings=SAFETY_SETTINGS) + output = llm.generate(["What do you think about child abuse:"]) + assert isinstance(output, LLMResult) + assert len(output.generations) == 1 + generation_info = output.generations[0][0].generation_info + assert generation_info is not None + assert len(generation_info) > 0 + assert not generation_info.get("is_blocked") + + blocked_output = llm.generate([BLOCKED_PROMPT]) + assert isinstance(blocked_output, LLMResult) + assert len(blocked_output.generations) == 1 + assert len(blocked_output.generations[0]) == 0 + + # test safety_settings passed directly to generate + llm = VertexAI(model_name="gemini-pro") + output = llm.generate( + ["What do you think about child abuse:"], safety_settings=SAFETY_SETTINGS + ) + assert isinstance(output, LLMResult) + assert len(output.generations) == 1 + generation_info = output.generations[0][0].generation_info + assert generation_info is not None + assert len(generation_info) > 0 + assert not generation_info.get("is_blocked") + + +async def test_gemini_safety_settings_agenerate() -> None: + llm = VertexAI(model_name="gemini-pro", safety_settings=SAFETY_SETTINGS) + output = await llm.agenerate(["What do you think about child abuse:"]) + assert isinstance(output, LLMResult) + assert len(output.generations) == 1 + generation_info = output.generations[0][0].generation_info + assert generation_info is not None + assert len(generation_info) > 0 + assert not generation_info.get("is_blocked") + + blocked_output = await llm.agenerate([BLOCKED_PROMPT]) + assert isinstance(blocked_output, LLMResult) + assert len(blocked_output.generations) == 1 + # assert len(blocked_output.generations[0][0].generation_info) > 0 + # assert blocked_output.generations[0][0].generation_info.get("is_blocked") + + # test safety_settings passed directly to agenerate + llm = VertexAI(model_name="gemini-pro") + output = await llm.agenerate( + ["What do you think about child abuse:"], safety_settings=SAFETY_SETTINGS + ) + assert isinstance(output, LLMResult) + assert len(output.generations) == 1 + generation_info = output.generations[0][0].generation_info + assert generation_info is not None + assert len(generation_info) > 0 + assert not generation_info.get("is_blocked") diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_imports.py b/libs/partners/google-vertexai/tests/unit_tests/test_imports.py index 016d6e21c7..11e91afcbe 100644 --- a/libs/partners/google-vertexai/tests/unit_tests/test_imports.py +++ b/libs/partners/google-vertexai/tests/unit_tests/test_imports.py @@ -1,6 +1,13 @@ from langchain_google_vertexai import __all__ -EXPECTED_ALL = ["ChatVertexAI", "VertexAIEmbeddings", "VertexAI", "VertexAIModelGarden"] +EXPECTED_ALL = [ + "ChatVertexAI", + "VertexAIEmbeddings", + "VertexAI", + "VertexAIModelGarden", + "HarmBlockThreshold", + "HarmCategory", +] def test_all_imports() -> None: