From 248c5b84ee9f9954140c28f87ac331e51448bb3e Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Sun, 25 Feb 2024 21:58:05 -0800 Subject: [PATCH] google-genai, google-vertexai: move to langchain-google (#17899) These packages have moved to https://github.com/langchain-ai/langchain-google Left tombstone readmes incase anyone ends up at the "Source Code" link from old pypi releases. Can keep these around for a few months. --- libs/partners/google-genai/.gitignore | 1 - libs/partners/google-genai/LICENSE | 21 - libs/partners/google-genai/Makefile | 61 - libs/partners/google-genai/README.md | 79 +- .../langchain_google_genai/__init__.py | 69 - .../langchain_google_genai/_common.py | 4 - .../langchain_google_genai/_enums.py | 6 - .../langchain_google_genai/_function_utils.py | 116 - .../langchain_google_genai/chat_models.py | 676 ----- .../langchain_google_genai/embeddings.py | 115 - .../langchain_google_genai/llms.py | 350 --- .../langchain_google_genai/py.typed | 0 libs/partners/google-genai/poetry.lock | 1278 ---------- libs/partners/google-genai/pyproject.toml | 107 - .../google-genai/scripts/check_imports.py | 17 - .../google-genai/scripts/check_pydantic.sh | 27 - .../google-genai/scripts/lint_imports.sh | 17 - libs/partners/google-genai/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../integration_tests/test_chat_models.py | 228 -- .../tests/integration_tests/test_compile.py | 7 - .../integration_tests/test_embeddings.py | 98 - .../integration_tests/test_function_call.py | 84 - .../tests/integration_tests/test_llms.py | 106 - .../google-genai/tests/unit_tests/__init__.py | 0 .../tests/unit_tests/test_chat_models.py | 75 - .../tests/unit_tests/test_embeddings.py | 38 - .../tests/unit_tests/test_imports.py | 13 - .../tests/unit_tests/test_llms.py | 8 - libs/partners/google-vertexai/.gitignore | 1 - libs/partners/google-vertexai/LICENSE | 21 - libs/partners/google-vertexai/Makefile | 61 - libs/partners/google-vertexai/README.md | 101 +- .../langchain_google_vertexai/__init__.py | 17 - .../langchain_google_vertexai/_enums.py | 6 - .../langchain_google_vertexai/_utils.py | 132 - .../langchain_google_vertexai/chains.py | 111 - .../langchain_google_vertexai/chat_models.py | 555 ---- .../langchain_google_vertexai/embeddings.py | 336 --- .../functions_utils.py | 152 -- .../langchain_google_vertexai/llms.py | 555 ---- .../langchain_google_vertexai/py.typed | 0 libs/partners/google-vertexai/poetry.lock | 2261 ----------------- libs/partners/google-vertexai/pyproject.toml | 110 - .../google-vertexai/scripts/check_imports.py | 17 - .../google-vertexai/scripts/check_pydantic.sh | 27 - .../google-vertexai/scripts/lint_imports.sh | 17 - .../google-vertexai/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../integration_tests/test_chat_models.py | 260 -- .../tests/integration_tests/test_compile.py | 7 - .../integration_tests/test_embeddings.py | 70 - .../tests/integration_tests/test_llms.py | 195 -- .../integration_tests/test_llms_safety.py | 97 - .../tests/integration_tests/test_tools.py | 172 -- .../tests/unit_tests/__init__.py | 0 .../tests/unit_tests/test_chat_models.py | 318 --- .../tests/unit_tests/test_imports.py | 16 - 58 files changed, 4 insertions(+), 9212 deletions(-) delete mode 100644 libs/partners/google-genai/.gitignore delete mode 100644 libs/partners/google-genai/LICENSE delete mode 100644 libs/partners/google-genai/Makefile delete mode 100644 libs/partners/google-genai/langchain_google_genai/__init__.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/_common.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/_enums.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/_function_utils.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/chat_models.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/embeddings.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/llms.py delete mode 100644 libs/partners/google-genai/langchain_google_genai/py.typed delete mode 100644 libs/partners/google-genai/poetry.lock delete mode 100644 libs/partners/google-genai/pyproject.toml delete mode 100644 libs/partners/google-genai/scripts/check_imports.py delete mode 100755 libs/partners/google-genai/scripts/check_pydantic.sh delete mode 100755 libs/partners/google-genai/scripts/lint_imports.sh delete mode 100644 libs/partners/google-genai/tests/__init__.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/__init__.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/test_chat_models.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/test_compile.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/test_embeddings.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/test_function_call.py delete mode 100644 libs/partners/google-genai/tests/integration_tests/test_llms.py delete mode 100644 libs/partners/google-genai/tests/unit_tests/__init__.py delete mode 100644 libs/partners/google-genai/tests/unit_tests/test_chat_models.py delete mode 100644 libs/partners/google-genai/tests/unit_tests/test_embeddings.py delete mode 100644 libs/partners/google-genai/tests/unit_tests/test_imports.py delete mode 100644 libs/partners/google-genai/tests/unit_tests/test_llms.py delete mode 100644 libs/partners/google-vertexai/.gitignore delete mode 100644 libs/partners/google-vertexai/LICENSE delete mode 100644 libs/partners/google-vertexai/Makefile delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/__init__.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/_enums.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/_utils.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/chains.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/functions_utils.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/llms.py delete mode 100644 libs/partners/google-vertexai/langchain_google_vertexai/py.typed delete mode 100644 libs/partners/google-vertexai/poetry.lock delete mode 100644 libs/partners/google-vertexai/pyproject.toml delete mode 100644 libs/partners/google-vertexai/scripts/check_imports.py delete mode 100755 libs/partners/google-vertexai/scripts/check_pydantic.sh delete mode 100755 libs/partners/google-vertexai/scripts/lint_imports.sh delete mode 100644 libs/partners/google-vertexai/tests/__init__.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/__init__.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_compile.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_llms.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_llms_safety.py delete mode 100644 libs/partners/google-vertexai/tests/integration_tests/test_tools.py delete mode 100644 libs/partners/google-vertexai/tests/unit_tests/__init__.py delete mode 100644 libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py delete mode 100644 libs/partners/google-vertexai/tests/unit_tests/test_imports.py diff --git a/libs/partners/google-genai/.gitignore b/libs/partners/google-genai/.gitignore deleted file mode 100644 index bee8a64b79a99..0000000000000 --- a/libs/partners/google-genai/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/libs/partners/google-genai/LICENSE b/libs/partners/google-genai/LICENSE deleted file mode 100644 index 426b65090341f..0000000000000 --- a/libs/partners/google-genai/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 LangChain, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/libs/partners/google-genai/Makefile b/libs/partners/google-genai/Makefile deleted file mode 100644 index 132eb9eb89892..0000000000000 --- a/libs/partners/google-genai/Makefile +++ /dev/null @@ -1,61 +0,0 @@ -.PHONY: all format lint test tests integration_tests help - -# Default target executed when no arguments are given to make. -all: help - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ - -test: - poetry run pytest $(TEST_FILE) - -tests: - poetry run pytest $(TEST_FILE) - -check_imports: $(shell find langchain_google_genai -name '*.py') - poetry run python ./scripts/check_imports.py $^ - -integration_tests: - poetry run pytest tests/integration_tests - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_google_genai -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - ./scripts/check_pydantic.sh . - ./scripts/lint_imports.sh - poetry run ruff . - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff - [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) - -format format_diff: - poetry run ruff format $(PYTHON_FILES) - poetry run ruff --select I --fix $(PYTHON_FILES) - -spell_check: - poetry run codespell --toml pyproject.toml - -spell_fix: - poetry run codespell --toml pyproject.toml -w - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'format - run code formatters' - @echo 'lint - run linters' - @echo 'test - run unit tests' - @echo 'tests - run unit tests' - @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/google-genai/README.md b/libs/partners/google-genai/README.md index 7de2b6f693f2f..088b699ee2e71 100644 --- a/libs/partners/google-genai/README.md +++ b/libs/partners/google-genai/README.md @@ -1,78 +1,3 @@ -# langchain-google-genai +This package has moved! -This package contains the LangChain integrations for Gemini through their generative-ai SDK. - -## Installation - -```bash -pip install -U langchain-google-genai -``` - -### Image utilities -To use image utility methods, like loading images from GCS urls, install with extras group 'images': - -```bash -pip install -e "langchain-google-genai[images]" -``` - -## Chat Models - -This package contains the `ChatGoogleGenerativeAI` class, which is the recommended way to interface with the Google Gemini series of models. - -To use, install the requirements, and configure your environment. - -```bash -export GOOGLE_API_KEY=your-api-key -``` - -Then initialize - -```python -from langchain_google_genai import ChatGoogleGenerativeAI - -llm = ChatGoogleGenerativeAI(model="gemini-pro") -llm.invoke("Sing a ballad of LangChain.") -``` - -#### Multimodal inputs - -Gemini vision model supports image inputs when providing a single chat message. Example: - -``` -from langchain_core.messages import HumanMessage -from langchain_google_genai import ChatGoogleGenerativeAI - -llm = ChatGoogleGenerativeAI(model="gemini-pro-vision") -# example -message = HumanMessage( - content=[ - { - "type": "text", - "text": "What's in this image?", - }, # You can optionally provide text parts - {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"}, - ] -) -llm.invoke([message]) -``` - -The value of `image_url` can be any of the following: - -- A public image URL -- An accessible gcs file (e.g., "gcs://path/to/file.png") -- A local file path -- A base64 encoded image (e.g., `data:image/png;base64,abcd124`) -- A PIL image - - - -## Embeddings - -This package also adds support for google's embeddings models. - -``` -from langchain_google_genai import GoogleGenerativeAIEmbeddings - -embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") -embeddings.embed_query("hello, world!") -``` \ No newline at end of file +https://github.com/langchain-ai/langchain-google/tree/main/libs/genai \ No newline at end of file diff --git a/libs/partners/google-genai/langchain_google_genai/__init__.py b/libs/partners/google-genai/langchain_google_genai/__init__.py deleted file mode 100644 index 187f7e3e036b9..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/__init__.py +++ /dev/null @@ -1,69 +0,0 @@ -"""**LangChain Google Generative AI Integration** - -This module integrates Google's Generative AI models, specifically the Gemini series, with the LangChain framework. It provides classes for interacting with chat models and generating embeddings, leveraging Google's advanced AI capabilities. - -**Chat Models** - -The `ChatGoogleGenerativeAI` class is the primary interface for interacting with Google's Gemini chat models. It allows users to send and receive messages using a specified Gemini model, suitable for various conversational AI applications. - -**LLMs** - -The `GoogleGenerativeAI` class is the primary interface for interacting with Google's Gemini LLMs. It allows users to generate text using a specified Gemini model. - -**Embeddings** - -The `GoogleGenerativeAIEmbeddings` class provides functionalities to generate embeddings using Google's models. -These embeddings can be used for a range of NLP tasks, including semantic analysis, similarity comparisons, and more. -**Installation** - -To install the package, use pip: - -```python -pip install -U langchain-google-genai -``` -## Using Chat Models - -After setting up your environment with the required API key, you can interact with the Google Gemini models. - -```python -from langchain_google_genai import ChatGoogleGenerativeAI - -llm = ChatGoogleGenerativeAI(model="gemini-pro") -llm.invoke("Sing a ballad of LangChain.") -``` - -## Using LLMs - -The package also supports generating text with Google's models. - -```python -from langchain_google_genai import GoogleGenerativeAI - -llm = GoogleGenerativeAI(model="gemini-pro") -llm.invoke("Once upon a time, a library called LangChain") -``` - -## Embedding Generation - -The package also supports creating embeddings with Google's models, useful for textual similarity and other NLP applications. - -```python -from langchain_google_genai import GoogleGenerativeAIEmbeddings - -embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") -embeddings.embed_query("hello, world!") -``` -""" # noqa: E501 - -from langchain_google_genai._enums import HarmBlockThreshold, HarmCategory -from langchain_google_genai.chat_models import ChatGoogleGenerativeAI -from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings -from langchain_google_genai.llms import GoogleGenerativeAI - -__all__ = [ - "ChatGoogleGenerativeAI", - "GoogleGenerativeAIEmbeddings", - "GoogleGenerativeAI", - "HarmBlockThreshold", - "HarmCategory", -] diff --git a/libs/partners/google-genai/langchain_google_genai/_common.py b/libs/partners/google-genai/langchain_google_genai/_common.py deleted file mode 100644 index d7bae390b23a4..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/_common.py +++ /dev/null @@ -1,4 +0,0 @@ -class GoogleGenerativeAIError(Exception): - """ - Custom exception class for errors associated with the `Google GenAI` API. - """ diff --git a/libs/partners/google-genai/langchain_google_genai/_enums.py b/libs/partners/google-genai/langchain_google_genai/_enums.py deleted file mode 100644 index b2a1de2614e99..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/_enums.py +++ /dev/null @@ -1,6 +0,0 @@ -from google.generativeai.types.safety_types import ( # type: ignore - HarmBlockThreshold, - HarmCategory, -) - -__all__ = ["HarmBlockThreshold", "HarmCategory"] diff --git a/libs/partners/google-genai/langchain_google_genai/_function_utils.py b/libs/partners/google-genai/langchain_google_genai/_function_utils.py deleted file mode 100644 index d67194536d6c3..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/_function_utils.py +++ /dev/null @@ -1,116 +0,0 @@ -from __future__ import annotations - -from typing import ( - Dict, - List, - Type, - Union, -) - -import google.ai.generativelanguage as glm -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.tools import BaseTool -from langchain_core.utils.json_schema import dereference_refs - -FunctionCallType = Union[BaseTool, Type[BaseModel], Dict] - -TYPE_ENUM = { - "string": glm.Type.STRING, - "number": glm.Type.NUMBER, - "integer": glm.Type.INTEGER, - "boolean": glm.Type.BOOLEAN, - "array": glm.Type.ARRAY, - "object": glm.Type.OBJECT, -} - - -def convert_to_genai_function_declarations( - function_calls: List[FunctionCallType], -) -> List[glm.Tool]: - return [ - glm.Tool( - function_declarations=[_convert_to_genai_function(fc)], - ) - for fc in function_calls - ] - - -def _convert_to_genai_function(fc: FunctionCallType) -> glm.FunctionDeclaration: - if isinstance(fc, BaseTool): - return _convert_tool_to_genai_function(fc) - elif isinstance(fc, type) and issubclass(fc, BaseModel): - return _convert_pydantic_to_genai_function(fc) - elif isinstance(fc, dict): - return glm.FunctionDeclaration( - name=fc["name"], - description=fc.get("description"), - parameters={ - "properties": { - k: { - "type_": TYPE_ENUM[v["type"]], - "description": v.get("description"), - } - for k, v in fc["parameters"]["properties"].items() - }, - "required": fc["parameters"].get("required", []), - "type_": TYPE_ENUM[fc["parameters"]["type"]], - }, - ) - else: - raise ValueError(f"Unsupported function call type {fc}") - - -def _convert_tool_to_genai_function(tool: BaseTool) -> glm.FunctionDeclaration: - if tool.args_schema: - schema = dereference_refs(tool.args_schema.schema()) - schema.pop("definitions", None) - - return glm.FunctionDeclaration( - name=tool.name or schema["title"], - description=tool.description or schema["description"], - parameters={ - "properties": { - k: { - "type_": TYPE_ENUM[v["type"]], - "description": v.get("description"), - } - for k, v in schema["properties"].items() - }, - "required": schema["required"], - "type_": TYPE_ENUM[schema["type"]], - }, - ) - else: - return glm.FunctionDeclaration( - name=tool.name, - description=tool.description, - parameters={ - "properties": { - "__arg1": {"type_": TYPE_ENUM["string"]}, - }, - "required": ["__arg1"], - "type_": TYPE_ENUM["object"], - }, - ) - - -def _convert_pydantic_to_genai_function( - pydantic_model: Type[BaseModel], -) -> glm.FunctionDeclaration: - schema = dereference_refs(pydantic_model.schema()) - schema.pop("definitions", None) - return glm.FunctionDeclaration( - name=schema["title"], - description=schema.get("description", ""), - parameters={ - "properties": { - k: { - "type_": TYPE_ENUM[v["type"]], - "description": v.get("description"), - } - for k, v in schema["properties"].items() - }, - "required": schema["required"], - "type_": TYPE_ENUM[schema["type"]], - }, - ) diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py deleted file mode 100644 index 4f0e9f0a22300..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ /dev/null @@ -1,676 +0,0 @@ -from __future__ import annotations - -import base64 -import json -import logging -import os -from io import BytesIO -from typing import ( - Any, - AsyncIterator, - Callable, - Dict, - Iterator, - List, - Mapping, - Optional, - Sequence, - Tuple, - Union, - cast, -) -from urllib.parse import urlparse - -import google.ai.generativelanguage as glm -import google.api_core - -# TODO: remove ignore once the google package is published with types -import google.generativeai as genai # type: ignore[import] -import proto # type: ignore[import] -import requests -from langchain_core.callbacks.manager import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, - FunctionMessage, - HumanMessage, - SystemMessage, -) -from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import SecretStr, root_validator -from langchain_core.utils import get_from_dict_or_env -from tenacity import ( - before_sleep_log, - retry, - retry_if_exception_type, - stop_after_attempt, - wait_exponential, -) - -from langchain_google_genai._common import GoogleGenerativeAIError -from langchain_google_genai._function_utils import ( - convert_to_genai_function_declarations, -) -from langchain_google_genai.llms import GoogleModelFamily, _BaseGoogleGenerativeAI - -IMAGE_TYPES: Tuple = () -try: - import PIL - from PIL.Image import Image - - IMAGE_TYPES = IMAGE_TYPES + (Image,) -except ImportError: - PIL = None # type: ignore - Image = None # type: ignore - -logger = logging.getLogger(__name__) - - -class ChatGoogleGenerativeAIError(GoogleGenerativeAIError): - """ - Custom exception class for errors associated with the `Google GenAI` API. - - This exception is raised when there are specific issues related to the - Google genai API usage in the ChatGoogleGenerativeAI class, such as unsupported - message types or roles. - """ - - -def _create_retry_decorator() -> Callable[[Any], Any]: - """ - Creates and returns a preconfigured tenacity retry decorator. - - The retry decorator is configured to handle specific Google API exceptions - such as ResourceExhausted and ServiceUnavailable. It uses an exponential - backoff strategy for retries. - - Returns: - Callable[[Any], Any]: A retry decorator configured for handling specific - Google API exceptions. - """ - multiplier = 2 - min_seconds = 1 - max_seconds = 60 - max_retries = 10 - - return retry( - reraise=True, - stop=stop_after_attempt(max_retries), - wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds), - retry=( - retry_if_exception_type(google.api_core.exceptions.ResourceExhausted) - | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable) - | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError) - ), - before_sleep=before_sleep_log(logger, logging.WARNING), - ) - - -def _chat_with_retry(generation_method: Callable, **kwargs: Any) -> Any: - """ - Executes a chat generation method with retry logic using tenacity. - - This function is a wrapper that applies a retry mechanism to a provided - chat generation function. It is useful for handling intermittent issues - like network errors or temporary service unavailability. - - Args: - generation_method (Callable): The chat generation method to be executed. - **kwargs (Any): Additional keyword arguments to pass to the generation method. - - Returns: - Any: The result from the chat generation method. - """ - retry_decorator = _create_retry_decorator() - - @retry_decorator - def _chat_with_retry(**kwargs: Any) -> Any: - try: - return generation_method(**kwargs) - # Do not retry for these errors. - except google.api_core.exceptions.FailedPrecondition as exc: - if "location is not supported" in exc.message: - error_msg = ( - "Your location is not supported by google-generativeai " - "at the moment. Try to use ChatVertexAI LLM from " - "langchain_google_vertexai." - ) - raise ValueError(error_msg) - - except google.api_core.exceptions.InvalidArgument as e: - raise ChatGoogleGenerativeAIError( - f"Invalid argument provided to Gemini: {e}" - ) from e - except Exception as e: - raise e - - return _chat_with_retry(**kwargs) - - -async def _achat_with_retry(generation_method: Callable, **kwargs: Any) -> Any: - """ - Executes a chat generation method with retry logic using tenacity. - - This function is a wrapper that applies a retry mechanism to a provided - chat generation function. It is useful for handling intermittent issues - like network errors or temporary service unavailability. - - Args: - generation_method (Callable): The chat generation method to be executed. - **kwargs (Any): Additional keyword arguments to pass to the generation method. - - Returns: - Any: The result from the chat generation method. - """ - retry_decorator = _create_retry_decorator() - from google.api_core.exceptions import InvalidArgument # type: ignore - - @retry_decorator - async def _achat_with_retry(**kwargs: Any) -> Any: - try: - return await generation_method(**kwargs) - except InvalidArgument as e: - # Do not retry for these errors. - raise ChatGoogleGenerativeAIError( - f"Invalid argument provided to Gemini: {e}" - ) from e - except Exception as e: - raise e - - return await _achat_with_retry(**kwargs) - - -def _is_openai_parts_format(part: dict) -> bool: - return "type" in part - - -def _is_vision_model(model: str) -> bool: - return "vision" in model - - -def _is_url(s: str) -> bool: - try: - result = urlparse(s) - return all([result.scheme, result.netloc]) - except Exception as e: - logger.debug(f"Unable to parse URL: {e}") - return False - - -def _is_b64(s: str) -> bool: - return s.startswith("data:image") - - -def _load_image_from_gcs(path: str, project: Optional[str] = None) -> Image: - try: - from google.cloud import storage # type: ignore[attr-defined] - except ImportError: - raise ImportError( - "google-cloud-storage is required to load images from GCS." - " Install it with `pip install google-cloud-storage`" - ) - if PIL is None: - raise ImportError( - "PIL is required to load images. Please install it " - "with `pip install pillow`" - ) - - gcs_client = storage.Client(project=project) - pieces = path.split("/") - blobs = list(gcs_client.list_blobs(pieces[2], prefix="/".join(pieces[3:]))) - if len(blobs) > 1: - raise ValueError(f"Found more than one candidate for {path}!") - img_bytes = blobs[0].download_as_bytes() - return PIL.Image.open(BytesIO(img_bytes)) - - -def _url_to_pil(image_source: str) -> Image: - if PIL is None: - raise ImportError( - "PIL is required to load images. Please install it " - "with `pip install pillow`" - ) - try: - if isinstance(image_source, IMAGE_TYPES): - return image_source # type: ignore[return-value] - elif _is_url(image_source): - if image_source.startswith("gs://"): - return _load_image_from_gcs(image_source) - response = requests.get(image_source) - response.raise_for_status() - return PIL.Image.open(BytesIO(response.content)) - elif _is_b64(image_source): - _, encoded = image_source.split(",", 1) - data = base64.b64decode(encoded) - return PIL.Image.open(BytesIO(data)) - elif os.path.exists(image_source): - return PIL.Image.open(image_source) - else: - raise ValueError( - "The provided string is not a valid URL, base64, or file path." - ) - except Exception as e: - raise ValueError(f"Unable to process the provided image source: {e}") - - -def _convert_to_parts( - raw_content: Union[str, Sequence[Union[str, dict]]], -) -> List[genai.types.PartType]: - """Converts a list of LangChain messages into a google parts.""" - parts = [] - content = [raw_content] if isinstance(raw_content, str) else raw_content - for part in content: - if isinstance(part, str): - parts.append(genai.types.PartDict(text=part)) - elif isinstance(part, Mapping): - # OpenAI Format - if _is_openai_parts_format(part): - if part["type"] == "text": - parts.append({"text": part["text"]}) - elif part["type"] == "image_url": - img_url = part["image_url"] - if isinstance(img_url, dict): - if "url" not in img_url: - raise ValueError( - f"Unrecognized message image format: {img_url}" - ) - img_url = img_url["url"] - parts.append({"inline_data": _url_to_pil(img_url)}) - else: - raise ValueError(f"Unrecognized message part type: {part['type']}") - else: - # Yolo - logger.warning( - "Unrecognized message part format. Assuming it's a text part." - ) - parts.append(part) - else: - # TODO: Maybe some of Google's native stuff - # would hit this branch. - raise ChatGoogleGenerativeAIError( - "Gemini only supports text and inline_data parts." - ) - return parts - - -def _parse_chat_history( - input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False -) -> List[genai.types.ContentDict]: - messages: List[genai.types.MessageDict] = [] - - raw_system_message: Optional[SystemMessage] = None - for i, message in enumerate(input_messages): - if ( - i == 0 - and isinstance(message, SystemMessage) - and not convert_system_message_to_human - ): - raise ValueError( - """SystemMessages are not yet supported! - -To automatically convert the leading SystemMessage to a HumanMessage, -set `convert_system_message_to_human` to True. Example: - -llm = ChatGoogleGenerativeAI(model="gemini-pro", convert_system_message_to_human=True) -""" - ) - elif i == 0 and isinstance(message, SystemMessage): - raw_system_message = message - continue - elif isinstance(message, AIMessage): - role = "model" - raw_function_call = message.additional_kwargs.get("function_call") - if raw_function_call: - function_call = glm.FunctionCall( - { - "name": raw_function_call["name"], - "args": json.loads(raw_function_call["arguments"]), - } - ) - parts = [glm.Part(function_call=function_call)] - else: - parts = _convert_to_parts(message.content) - elif isinstance(message, HumanMessage): - role = "user" - parts = _convert_to_parts(message.content) - elif isinstance(message, FunctionMessage): - role = "user" - response: Any - if not isinstance(message.content, str): - response = message.content - else: - try: - response = json.loads(message.content) - except json.JSONDecodeError: - response = message.content # leave as str representation - parts = [ - glm.Part( - function_response=glm.FunctionResponse( - name=message.name, - response=( - {"output": response} - if not isinstance(response, dict) - else response - ), - ) - ) - ] - else: - raise ValueError( - f"Unexpected message with type {type(message)} at the position {i}." - ) - - if raw_system_message: - if role == "model": - raise ValueError( - "SystemMessage should be followed by a HumanMessage and " - "not by AIMessage." - ) - parts = _convert_to_parts(raw_system_message.content) + parts - raw_system_message = None - messages.append({"role": role, "parts": parts}) - return messages - - -def _parse_response_candidate( - response_candidate: glm.Candidate, stream: bool -) -> AIMessage: - first_part = response_candidate.content.parts[0] - if first_part.function_call: - function_call = proto.Message.to_dict(first_part.function_call) - function_call["arguments"] = json.dumps(function_call.pop("args", {})) - return (AIMessageChunk if stream else AIMessage)( - content="", additional_kwargs={"function_call": function_call} - ) - else: - parts = response_candidate.content.parts - - if len(parts) == 1 and parts[0].text: - content: Union[str, List[Union[str, Dict]]] = parts[0].text - else: - content = [proto.Message.to_dict(part) for part in parts] - return (AIMessageChunk if stream else AIMessage)( - content=content, additional_kwargs={} - ) - - -def _response_to_result( - response: glm.GenerateContentResponse, - stream: bool = False, -) -> ChatResult: - """Converts a PaLM API response into a LangChain ChatResult.""" - llm_output = {"prompt_feedback": proto.Message.to_dict(response.prompt_feedback)} - - generations: List[ChatGeneration] = [] - - for candidate in response.candidates: - generation_info = {} - if candidate.finish_reason: - generation_info["finish_reason"] = candidate.finish_reason.name - generation_info["safety_ratings"] = [ - proto.Message.to_dict(safety_rating, use_integers_for_enums=False) - for safety_rating in candidate.safety_ratings - ] - generations.append( - (ChatGenerationChunk if stream else ChatGeneration)( - message=_parse_response_candidate(candidate, stream=stream), - generation_info=generation_info, - ) - ) - if not response.candidates: - # Likely a "prompt feedback" violation (e.g., toxic input) - # Raising an error would be different than how OpenAI handles it, - # so we'll just log a warning and continue with an empty message. - logger.warning( - "Gemini produced an empty response. Continuing with empty message\n" - f"Feedback: {response.prompt_feedback}" - ) - generations = [ - (ChatGenerationChunk if stream else ChatGeneration)( - message=(AIMessageChunk if stream else AIMessage)(content=""), - generation_info={}, - ) - ] - return ChatResult(generations=generations, llm_output=llm_output) - - -class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): - """`Google Generative AI` Chat models API. - - To use, you must have either: - - 1. The ``GOOGLE_API_KEY``` environment variable set with your API key, or - 2. Pass your API key using the google_api_key kwarg to the ChatGoogle - constructor. - - Example: - .. code-block:: python - - from langchain_google_genai import ChatGoogleGenerativeAI - chat = ChatGoogleGenerativeAI(model="gemini-pro") - chat.invoke("Write me a ballad about LangChain") - - """ - - client: Any #: :meta private: - - convert_system_message_to_human: bool = False - """Whether to merge any leading SystemMessage into the following HumanMessage. - - Gemini does not support system messages; any unsupported messages will - raise an error.""" - - class Config: - allow_population_by_field_name = True - - @property - def lc_secrets(self) -> Dict[str, str]: - return {"google_api_key": "GOOGLE_API_KEY"} - - @property - def _llm_type(self) -> str: - return "chat-google-generative-ai" - - @classmethod - def is_lc_serializable(self) -> bool: - return True - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validates params and passes them to google-generativeai package.""" - google_api_key = get_from_dict_or_env( - values, "google_api_key", "GOOGLE_API_KEY" - ) - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() - - genai.configure( - api_key=google_api_key, - transport=values.get("transport"), - client_options=values.get("client_options"), - ) - if ( - values.get("temperature") is not None - and not 0 <= values["temperature"] <= 1 - ): - raise ValueError("temperature must be in the range [0.0, 1.0]") - - if values.get("top_p") is not None and not 0 <= values["top_p"] <= 1: - raise ValueError("top_p must be in the range [0.0, 1.0]") - - if values.get("top_k") is not None and values["top_k"] <= 0: - raise ValueError("top_k must be positive") - model = values["model"] - values["client"] = genai.GenerativeModel(model_name=model) - return values - - @property - def _identifying_params(self) -> Dict[str, Any]: - """Get the identifying parameters.""" - return { - "model": self.model, - "temperature": self.temperature, - "top_k": self.top_k, - "n": self.n, - "safety_settings": self.safety_settings, - } - - def _prepare_params( - self, stop: Optional[List[str]], **kwargs: Any - ) -> Dict[str, Any]: - gen_config = { - k: v - for k, v in { - "candidate_count": self.n, - "temperature": self.temperature, - "stop_sequences": stop, - "max_output_tokens": self.max_output_tokens, - "top_k": self.top_k, - "top_p": self.top_p, - }.items() - if v is not None - } - if "generation_config" in kwargs: - gen_config = {**gen_config, **kwargs.pop("generation_config")} - params = {"generation_config": gen_config, **kwargs} - return params - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - params, chat, message = self._prepare_chat( - messages, - stop=stop, - **kwargs, - ) - response: genai.types.GenerateContentResponse = _chat_with_retry( - content=message, - **params, - generation_method=chat.send_message, - ) - return _response_to_result(response) - - async def _agenerate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - params, chat, message = self._prepare_chat( - messages, - stop=stop, - **kwargs, - ) - response: genai.types.GenerateContentResponse = await _achat_with_retry( - content=message, - **params, - generation_method=chat.send_message_async, - ) - return _response_to_result(response) - - def _stream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat( - messages, - stop=stop, - **kwargs, - ) - response: genai.types.GenerateContentResponse = _chat_with_retry( - content=message, - **params, - generation_method=chat.send_message, - stream=True, - ) - for chunk in response: - _chat_result = _response_to_result(chunk, stream=True) - gen = cast(ChatGenerationChunk, _chat_result.generations[0]) - - if run_manager: - run_manager.on_llm_new_token(gen.text) - yield gen - - async def _astream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> AsyncIterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat( - messages, - stop=stop, - **kwargs, - ) - async for chunk in await _achat_with_retry( - content=message, - **params, - generation_method=chat.send_message_async, - stream=True, - ): - _chat_result = _response_to_result(chunk, stream=True) - gen = cast(ChatGenerationChunk, _chat_result.generations[0]) - - if run_manager: - await run_manager.on_llm_new_token(gen.text) - yield gen - - def _prepare_chat( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - **kwargs: Any, - ) -> Tuple[Dict[str, Any], genai.ChatSession, genai.types.ContentDict]: - client = self.client - functions = kwargs.pop("functions", None) - safety_settings = kwargs.pop("safety_settings", self.safety_settings) - if functions or safety_settings: - tools = ( - convert_to_genai_function_declarations(functions) if functions else None - ) - client = genai.GenerativeModel( - model_name=self.model, tools=tools, safety_settings=safety_settings - ) - - params = self._prepare_params(stop, **kwargs) - history = _parse_chat_history( - messages, - convert_system_message_to_human=self.convert_system_message_to_human, - ) - message = history.pop() - chat = client.start_chat(history=history) - return params, chat, message - - def get_num_tokens(self, text: str) -> int: - """Get the number of tokens present in the text. - - Useful for checking if an input will fit in a model's context window. - - Args: - text: The string input to tokenize. - - Returns: - The integer number of tokens in the text. - """ - if self._model_family == GoogleModelFamily.GEMINI: - result = self.client.count_tokens(text) - token_count = result.total_tokens - else: - result = self.client.count_text_tokens(model=self.model, prompt=text) - token_count = result["token_count"] - - return token_count diff --git a/libs/partners/google-genai/langchain_google_genai/embeddings.py b/libs/partners/google-genai/langchain_google_genai/embeddings.py deleted file mode 100644 index 5e61581e0107d..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/embeddings.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Dict, List, Optional - -# TODO: remove ignore once the google package is published with types -import google.generativeai as genai # type: ignore[import] -from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator -from langchain_core.utils import get_from_dict_or_env - -from langchain_google_genai._common import GoogleGenerativeAIError - - -class GoogleGenerativeAIEmbeddings(BaseModel, Embeddings): - """`Google Generative AI Embeddings`. - - To use, you must have either: - - 1. The ``GOOGLE_API_KEY``` environment variable set with your API key, or - 2. Pass your API key using the google_api_key kwarg to the ChatGoogle - constructor. - - Example: - .. code-block:: python - - from langchain_google_genai import GoogleGenerativeAIEmbeddings - - embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") - embeddings.embed_query("What's our Q1 revenue?") - """ - - model: str = Field( - ..., - description="The name of the embedding model to use. " - "Example: models/embedding-001", - ) - task_type: Optional[str] = Field( - None, - description="The task type. Valid options include: " - "task_type_unspecified, retrieval_query, retrieval_document, " - "semantic_similarity, classification, and clustering", - ) - google_api_key: Optional[SecretStr] = Field( - None, - description="The Google API key to use. If not provided, " - "the GOOGLE_API_KEY environment variable will be used.", - ) - client_options: Optional[Dict] = Field( - None, - description=( - "A dictionary of client options to pass to the Google API client, " - "such as `api_endpoint`." - ), - ) - transport: Optional[str] = Field( - None, - description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", - ) - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validates params and passes them to google-generativeai package.""" - google_api_key = get_from_dict_or_env( - values, "google_api_key", "GOOGLE_API_KEY" - ) - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() - - genai.configure( - api_key=google_api_key, - transport=values.get("transport"), - client_options=values.get("client_options"), - ) - return values - - def _embed( - self, texts: List[str], task_type: str, title: Optional[str] = None - ) -> List[List[float]]: - task_type = self.task_type or "retrieval_document" - try: - result = genai.embed_content( - model=self.model, - content=texts, - task_type=task_type, - title=title, - ) - except Exception as e: - raise GoogleGenerativeAIError(f"Error embedding content: {e}") from e - return result["embedding"] - - def embed_documents( - self, texts: List[str], batch_size: int = 5 - ) -> List[List[float]]: - """Embed a list of strings. Vertex AI currently - sets a max batch size of 5 strings. - - Args: - texts: List[str] The list of strings to embed. - batch_size: [int] The batch size of embeddings to send to the model - - Returns: - List of embeddings, one for each text. - """ - task_type = self.task_type or "retrieval_document" - return self._embed(texts, task_type=task_type) - - def embed_query(self, text: str) -> List[float]: - """Embed a text. - - Args: - text: The text to embed. - - Returns: - Embedding for the text. - """ - task_type = self.task_type or "retrieval_query" - return self._embed([text], task_type=task_type)[0] diff --git a/libs/partners/google-genai/langchain_google_genai/llms.py b/libs/partners/google-genai/langchain_google_genai/llms.py deleted file mode 100644 index 9b48387314672..0000000000000 --- a/libs/partners/google-genai/langchain_google_genai/llms.py +++ /dev/null @@ -1,350 +0,0 @@ -from __future__ import annotations - -from enum import Enum, auto -from typing import Any, Callable, Dict, Iterator, List, Optional, Union - -import google.api_core -import google.generativeai as genai # type: ignore[import] -from langchain_core.callbacks import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models import LanguageModelInput -from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator -from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator -from langchain_core.utils import get_from_dict_or_env - -from langchain_google_genai._enums import ( - HarmBlockThreshold, - HarmCategory, -) - - -class GoogleModelFamily(str, Enum): - GEMINI = auto() - PALM = auto() - - @classmethod - def _missing_(cls, value: Any) -> Optional["GoogleModelFamily"]: - if "gemini" in value.lower(): - return GoogleModelFamily.GEMINI - elif "text-bison" in value.lower(): - return GoogleModelFamily.PALM - return None - - -def _create_retry_decorator( - llm: BaseLLM, - *, - max_retries: int = 1, - run_manager: Optional[ - Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] - ] = None, -) -> Callable[[Any], Any]: - """Creates a retry decorator for Vertex / Palm LLMs.""" - - errors = [ - google.api_core.exceptions.ResourceExhausted, - google.api_core.exceptions.ServiceUnavailable, - google.api_core.exceptions.Aborted, - google.api_core.exceptions.DeadlineExceeded, - google.api_core.exceptions.GoogleAPIError, - ] - decorator = create_base_retry_decorator( - error_types=errors, max_retries=max_retries, run_manager=run_manager - ) - return decorator - - -def _completion_with_retry( - llm: GoogleGenerativeAI, - prompt: LanguageModelInput, - is_gemini: bool = False, - stream: bool = False, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, -) -> Any: - """Use tenacity to retry the completion call.""" - retry_decorator = _create_retry_decorator( - llm, max_retries=llm.max_retries, run_manager=run_manager - ) - - @retry_decorator - def _completion_with_retry( - prompt: LanguageModelInput, is_gemini: bool, stream: bool, **kwargs: Any - ) -> Any: - generation_config = kwargs.get("generation_config", {}) - error_msg = ( - "Your location is not supported by google-generativeai at the moment. " - "Try to use VertexAI LLM from langchain_google_vertexai" - ) - try: - if is_gemini: - return llm.client.generate_content( - contents=prompt, - stream=stream, - generation_config=generation_config, - safety_settings=kwargs.pop("safety_settings", None), - ) - return llm.client.generate_text(prompt=prompt, **kwargs) - except google.api_core.exceptions.FailedPrecondition as exc: - if "location is not supported" in exc.message: - raise ValueError(error_msg) - - return _completion_with_retry( - prompt=prompt, is_gemini=is_gemini, stream=stream, **kwargs - ) - - -def _strip_erroneous_leading_spaces(text: str) -> str: - """Strip erroneous leading spaces from text. - - The PaLM API will sometimes erroneously return a single leading space in all - lines > 1. This function strips that space. - """ - has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:]) - if has_leading_space: - return text.replace("\n ", "\n") - else: - return text - - -class _BaseGoogleGenerativeAI(BaseModel): - """Base class for Google Generative AI LLMs""" - - model: str = Field( - ..., - description="""The name of the model to use. -Supported examples: - - gemini-pro - - models/text-bison-001""", - ) - """Model name to use.""" - google_api_key: Optional[SecretStr] = None - temperature: float = 0.7 - """Run inference with this temperature. Must by in the closed interval - [0.0, 1.0].""" - top_p: Optional[float] = None - """Decode using nucleus sampling: consider the smallest set of tokens whose - probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].""" - top_k: Optional[int] = None - """Decode using top-k sampling: consider the set of top_k most probable tokens. - Must be positive.""" - max_output_tokens: Optional[int] = None - """Maximum number of tokens to include in a candidate. Must be greater than zero. - If unset, will default to 64.""" - n: int = 1 - """Number of chat completions to generate for each prompt. Note that the API may - not return the full n completions if duplicates are generated.""" - max_retries: int = 6 - """The maximum number of retries to make when generating.""" - client_options: Optional[Dict] = Field( - None, - description=( - "A dictionary of client options to pass to the Google API client, " - "such as `api_endpoint`." - ), - ) - transport: Optional[str] = Field( - None, - description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", - ) - - safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None - """The default safety settings to use for all generations. - - For example: - - from google.generativeai.types.safety_types 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 lc_secrets(self) -> Dict[str, str]: - return {"google_api_key": "GOOGLE_API_KEY"} - - @property - def _model_family(self) -> str: - return GoogleModelFamily(self.model) - - @property - def _identifying_params(self) -> Dict[str, Any]: - """Get the identifying parameters.""" - return { - "model": self.model, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - "max_output_tokens": self.max_output_tokens, - "candidate_count": self.n, - } - - -class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): - """Google GenerativeAI models. - - Example: - .. code-block:: python - - from langchain_google_genai import GoogleGenerativeAI - llm = GoogleGenerativeAI(model="gemini-pro") - """ - - client: Any #: :meta private: - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validates params and passes them to google-generativeai package.""" - google_api_key = get_from_dict_or_env( - values, "google_api_key", "GOOGLE_API_KEY" - ) - model_name = values["model"] - - safety_settings = values["safety_settings"] - - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() - - genai.configure( - api_key=google_api_key, - transport=values.get("transport"), - client_options=values.get("client_options"), - ) - - if safety_settings and ( - not GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI - ): - raise ValueError("Safety settings are only supported for Gemini models") - - if GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI: - values["client"] = genai.GenerativeModel( - model_name=model_name, safety_settings=safety_settings - ) - else: - values["client"] = genai - - if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: - raise ValueError("temperature must be in the range [0.0, 1.0]") - - if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: - raise ValueError("top_p must be in the range [0.0, 1.0]") - - if values["top_k"] is not None and values["top_k"] <= 0: - raise ValueError("top_k must be positive") - - if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0: - raise ValueError("max_output_tokens must be greater than zero") - - return values - - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - generations: List[List[Generation]] = [] - generation_config = { - "stop_sequences": stop, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - "max_output_tokens": self.max_output_tokens, - "candidate_count": self.n, - } - for prompt in prompts: - if self._model_family == GoogleModelFamily.GEMINI: - res = _completion_with_retry( - self, - prompt=prompt, - stream=False, - is_gemini=True, - run_manager=run_manager, - generation_config=generation_config, - safety_settings=kwargs.pop("safety_settings", None), - ) - candidates = [ - "".join([p.text for p in c.content.parts]) for c in res.candidates - ] - generations.append([Generation(text=c) for c in candidates]) - else: - res = _completion_with_retry( - self, - model=self.model, - prompt=prompt, - stream=False, - is_gemini=False, - run_manager=run_manager, - **generation_config, - ) - prompt_generations = [] - for candidate in res.candidates: - raw_text = candidate["output"] - stripped_text = _strip_erroneous_leading_spaces(raw_text) - prompt_generations.append(Generation(text=stripped_text)) - generations.append(prompt_generations) - - return LLMResult(generations=generations) - - def _stream( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[GenerationChunk]: - generation_config = kwargs.get("generation_config", {}) - if stop: - generation_config["stop_sequences"] = stop - for stream_resp in _completion_with_retry( - 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-[extras] -images = ["pillow"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.9,<4.0" -content-hash = "f3b43f02c7300c3003347dbdfa9c07ddba988aab1387eda3efa02b2351c868d9" diff --git a/libs/partners/google-genai/pyproject.toml b/libs/partners/google-genai/pyproject.toml deleted file mode 100644 index 43248562bc849..0000000000000 --- a/libs/partners/google-genai/pyproject.toml +++ /dev/null @@ -1,107 +0,0 @@ -[tool.poetry] -name = "langchain-google-genai" -version = "0.0.9" -description = "An integration package connecting Google's genai package and LangChain" -authors = [] -readme = "README.md" -repository = "https://github.com/langchain-ai/langchain" -license = "MIT" - -[tool.poetry.urls] -"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/google-genai" - -[tool.poetry.dependencies] -python = ">=3.9,<4.0" -langchain-core = "^0.1" -google-generativeai = "^0.3.1" -pillow = { version = "^10.1.0", optional = true } - -[tool.poetry.extras] -images = ["pillow"] - 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-[tool.ruff.lint] -select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort - "T201", # print -] - -[tool.mypy] -disallow_untyped_defs = "True" - -[tool.coverage.run] -omit = ["tests/*"] - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" - -[tool.pytest.ini_options] -# --strict-markers will raise errors on unknown marks. -# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks -# -# https://docs.pytest.org/en/7.1.x/reference/reference.html -# --strict-config any warnings encountered while parsing the `pytest` -# section of the configuration file raise errors. -# -# https://github.com/tophat/syrupy -# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. -addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -# Registering custom markers. -# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers -markers = [ - "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", - "compile: mark placeholder test used to compile integration tests without running them", -] -asyncio_mode = "auto" diff --git a/libs/partners/google-genai/scripts/check_imports.py b/libs/partners/google-genai/scripts/check_imports.py deleted file mode 100644 index 365f5fa118da4..0000000000000 --- a/libs/partners/google-genai/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -import sys -import traceback -from importlib.machinery import SourceFileLoader - -if __name__ == "__main__": - files = sys.argv[1:] - has_failure = False - for file in files: - try: - SourceFileLoader("x", file).load_module() - except Exception: - has_faillure = True - print(file) # noqa: T201 - traceback.print_exc() - print() # noqa: T201 - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/google-genai/scripts/check_pydantic.sh b/libs/partners/google-genai/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/google-genai/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/google-genai/scripts/lint_imports.sh b/libs/partners/google-genai/scripts/lint_imports.sh deleted file mode 100755 index 695613c7ba8fd..0000000000000 --- a/libs/partners/google-genai/scripts/lint_imports.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -eu - -# Initialize a variable to keep track of errors -errors=0 - -# make sure not importing from langchain or langchain_experimental -git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) - -# Decide on an exit status based on the errors -if [ "$errors" -gt 0 ]; then - exit 1 -else - exit 0 -fi diff --git a/libs/partners/google-genai/tests/__init__.py b/libs/partners/google-genai/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-genai/tests/integration_tests/__init__.py b/libs/partners/google-genai/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-genai/tests/integration_tests/test_chat_models.py b/libs/partners/google-genai/tests/integration_tests/test_chat_models.py deleted file mode 100644 index 26e85aa10e181..0000000000000 --- a/libs/partners/google-genai/tests/integration_tests/test_chat_models.py +++ /dev/null @@ -1,228 +0,0 @@ -"""Test ChatGoogleGenerativeAI chat model.""" - -from typing import Generator - -import pytest -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage - -from langchain_google_genai import ( - ChatGoogleGenerativeAI, - HarmBlockThreshold, - HarmCategory, -) -from langchain_google_genai.chat_models import ChatGoogleGenerativeAIError - -_MODEL = "gemini-pro" # TODO: Use nano when it's available. -_VISION_MODEL = "gemini-pro-vision" -_B64_string = """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""" # noqa: E501 - - -def test_chat_google_genai_stream() -> None: - """Test streaming tokens from Gemini.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - for token in llm.stream("This is a test. Say 'foo'"): - assert isinstance(token.content, str) - - -async def test_chat_google_genai_astream() -> None: - """Test streaming tokens from Gemini.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - async for token in llm.astream("This is a test. Say 'foo'"): - assert isinstance(token.content, str) - - -async def test_chat_google_genai_abatch() -> None: - """Test streaming tokens from ChatGoogleGenerativeAI.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - result = await llm.abatch( - ["This is a test. Say 'foo'", "This is a test, say 'bar'"] - ) - for token in result: - assert isinstance(token.content, str) - - -async def test_chat_google_genai_abatch_tags() -> None: - """Test batch tokens from ChatGoogleGenerativeAI.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - result = await llm.abatch( - ["This is a test", "This is another test"], config={"tags": ["foo"]} - ) - for token in result: - assert isinstance(token.content, str) - - -def test_chat_google_genai_batch() -> None: - """Test batch tokens from ChatGoogleGenerativeAI.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - result = llm.batch(["This is a test. Say 'foo'", "This is a test, say 'bar'"]) - for token in result: - assert isinstance(token.content, str) - - -async def test_chat_google_genai_ainvoke() -> None: - """Test invoke tokens from ChatGoogleGenerativeAI.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - result = await llm.ainvoke("This is a test. Say 'foo'", config={"tags": ["foo"]}) - assert isinstance(result.content, str) - - -def test_chat_google_genai_invoke() -> None: - """Test invoke tokens from ChatGoogleGenerativeAI.""" - llm = ChatGoogleGenerativeAI(model=_MODEL) - - result = llm.invoke( - "This is a test. Say 'foo'", - config=dict(tags=["foo"]), - generation_config=dict(top_k=2, top_p=1, temperature=0.7), - ) - assert isinstance(result.content, str) - assert not result.content.startswith(" ") - - -def test_chat_google_genai_invoke_multimodal() -> None: - messages: list = [ - HumanMessage( - content=[ - { - "type": "text", - "text": "Guess what's in this picture! You have 3 guesses.", - }, - { - "type": "image_url", - "image_url": "data:image/png;base64," + _B64_string, - }, - ] - ), - ] - llm = ChatGoogleGenerativeAI(model=_VISION_MODEL) - response = llm.invoke(messages) - assert isinstance(response.content, str) - assert len(response.content.strip()) > 0 - - # Try streaming - for chunk in llm.stream(messages): - print(chunk) # noqa: T201 - assert isinstance(chunk.content, str) - assert len(chunk.content.strip()) > 0 - - -def test_chat_google_genai_invoke_multimodal_too_many_messages() -> None: - # Only supports 1 turn... - messages: list = [ - HumanMessage(content="Hi there"), - AIMessage(content="Hi, how are you?"), - HumanMessage( - content=[ - { - "type": "text", - "text": "I'm doing great! Guess what's in this picture!", - }, - { - "type": "image_url", - "image_url": "data:image/png;base64," + _B64_string, - }, - ] - ), - ] - llm = ChatGoogleGenerativeAI(model=_VISION_MODEL) - with pytest.raises(ChatGoogleGenerativeAIError): - llm.invoke(messages) - - -def test_chat_google_genai_invoke_multimodal_invalid_model() -> None: - # need the vision model to support this. - messages: list = [ - HumanMessage( - content=[ - { - "type": "text", - "text": "I'm doing great! Guess what's in this picture!", - }, - { - "type": "image_url", - "image_url": "data:image/png;base64," + _B64_string, - }, - ] - ), - ] - llm = ChatGoogleGenerativeAI(model=_MODEL) - with pytest.raises(ChatGoogleGenerativeAIError): - llm.invoke(messages) - - -def test_chat_google_genai_single_call_with_history() -> None: - model = ChatGoogleGenerativeAI(model=_MODEL) - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - response = model([message1, message2, message3]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -def test_chat_google_genai_system_message_error() -> None: - model = ChatGoogleGenerativeAI(model=_MODEL) - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content="You're supposed to answer math questions.") - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - with pytest.raises(ValueError): - model([system_message, message1, message2, message3]) - - -def test_chat_google_genai_system_message() -> None: - model = ChatGoogleGenerativeAI(model=_MODEL, convert_system_message_to_human=True) - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content="You're supposed to answer math questions.") - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - response = model([system_message, message1, message2, message3]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -def test_generativeai_get_num_tokens_gemini() -> None: - llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro") - output = llm.get_num_tokens("How are you?") - assert output == 4 - - -def test_safety_settings_gemini() -> None: - safety_settings = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, - } - # test with safety filters on bind - llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro").bind( - safety_settings=safety_settings - ) - output = llm.invoke("how to make a bomb?") - assert isinstance(output, AIMessage) - assert len(output.content) > 0 - - # test direct to stream - streamed_messages = [] - output_stream = llm.stream("how to make a bomb?", safety_settings=safety_settings) - assert isinstance(output_stream, Generator) - for message in output_stream: - streamed_messages.append(message) - assert len(streamed_messages) > 0 - - # test as init param - llm = ChatGoogleGenerativeAI( - temperature=0, model="gemini-pro", safety_settings=safety_settings - ) - out2 = llm.invoke("how to make a bomb") - assert isinstance(out2, AIMessage) - assert len(out2.content) > 0 diff --git a/libs/partners/google-genai/tests/integration_tests/test_compile.py b/libs/partners/google-genai/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0fbd..0000000000000 --- a/libs/partners/google-genai/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest - - -@pytest.mark.compile -def test_placeholder() -> None: - """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/google-genai/tests/integration_tests/test_embeddings.py b/libs/partners/google-genai/tests/integration_tests/test_embeddings.py deleted file mode 100644 index 13fbe9a632c85..0000000000000 --- a/libs/partners/google-genai/tests/integration_tests/test_embeddings.py +++ /dev/null @@ -1,98 +0,0 @@ -import numpy as np -import pytest - -from langchain_google_genai._common import GoogleGenerativeAIError -from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings - -_MODEL = "models/embedding-001" - - -@pytest.mark.parametrize( - "query", - [ - "Hi", - "This is a longer query string to test the embedding functionality of the" - " model against the pickle rick?", - ], -) -def test_embed_query_different_lengths(query: str) -> None: - """Test embedding queries of different lengths.""" - model = GoogleGenerativeAIEmbeddings(model=_MODEL) - result = model.embed_query(query) - assert len(result) == 768 - - -@pytest.mark.parametrize( - "query", - [ - "Hi", - "This is a longer query string to test the embedding functionality of the" - " model against the pickle rick?", - ], -) -async def test_aembed_query_different_lengths(query: str) -> None: - """Test embedding queries of different lengths.""" - model = GoogleGenerativeAIEmbeddings(model=_MODEL) - result = await model.aembed_query(query) - assert len(result) == 768 - - -def test_embed_documents() -> None: - """Test embedding a query.""" - model = GoogleGenerativeAIEmbeddings( - model=_MODEL, - ) - result = model.embed_documents(["Hello world", "Good day, world"]) - assert len(result) == 2 - assert len(result[0]) == 768 - assert len(result[1]) == 768 - - -async def test_aembed_documents() -> None: - """Test embedding a query.""" - model = GoogleGenerativeAIEmbeddings( - model=_MODEL, - ) - result = await model.aembed_documents(["Hello world", "Good day, world"]) - assert len(result) == 2 - assert len(result[0]) == 768 - assert len(result[1]) == 768 - - -def test_invalid_model_error_handling() -> None: - """Test error handling with an invalid model name.""" - with pytest.raises(GoogleGenerativeAIError): - GoogleGenerativeAIEmbeddings(model="invalid_model").embed_query("Hello world") - - -def test_invalid_api_key_error_handling() -> None: - """Test error handling with an invalid API key.""" - with pytest.raises(GoogleGenerativeAIError): - GoogleGenerativeAIEmbeddings( - model=_MODEL, google_api_key="invalid_key" - ).embed_query("Hello world") - - -def test_embed_documents_consistency() -> None: - """Test embedding consistency for the same document.""" - model = GoogleGenerativeAIEmbeddings(model=_MODEL) - doc = "Consistent document for testing" - result1 = model.embed_documents([doc]) - result2 = model.embed_documents([doc]) - assert result1 == result2 - - -def test_embed_documents_quality() -> None: - """Smoke test embedding quality by comparing similar and dissimilar documents.""" - model = GoogleGenerativeAIEmbeddings(model=_MODEL) - similar_docs = ["Document A", "Similar Document A"] - dissimilar_docs = ["Document A", "Completely Different Zebra"] - similar_embeddings = model.embed_documents(similar_docs) - dissimilar_embeddings = model.embed_documents(dissimilar_docs) - similar_distance = np.linalg.norm( - np.array(similar_embeddings[0]) - np.array(similar_embeddings[1]) - ) - dissimilar_distance = np.linalg.norm( - np.array(dissimilar_embeddings[0]) - np.array(dissimilar_embeddings[1]) - ) - assert similar_distance < dissimilar_distance diff --git a/libs/partners/google-genai/tests/integration_tests/test_function_call.py b/libs/partners/google-genai/tests/integration_tests/test_function_call.py deleted file mode 100644 index 57d0005a3bdc1..0000000000000 --- a/libs/partners/google-genai/tests/integration_tests/test_function_call.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Test ChatGoogleGenerativeAI function call.""" - -import json - -from langchain_core.messages import AIMessage -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.tools import tool - -from langchain_google_genai.chat_models import ( - ChatGoogleGenerativeAI, -) - - -def test_function_call() -> None: - functions = [ - { - "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"], - }, - } - ] - llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=functions) - res = llm.invoke("what weather is today in san francisco?") - assert res - assert res.additional_kwargs - assert "function_call" in res.additional_kwargs - assert "get_weather" == res.additional_kwargs["function_call"]["name"] - arguments_str = res.additional_kwargs["function_call"]["arguments"] - assert isinstance(arguments_str, str) - arguments = json.loads(arguments_str) - assert "location" in arguments - - -def test_tool_call() -> None: - @tool - def search_tool(query: str) -> str: - """Searches the web for `query` and returns the result.""" - raise NotImplementedError - - llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[search_tool]) - response = llm.invoke("weather in san francisco") - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - assert response.content == "" - function_call = response.additional_kwargs.get("function_call") - assert function_call - assert function_call["name"] == "search_tool" - arguments_str = function_call.get("arguments") - assert arguments_str - arguments = json.loads(arguments_str) - assert "query" in arguments - - -class MyModel(BaseModel): - name: str - age: int - - -def test_pydantic_call() -> None: - llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[MyModel]) - response = llm.invoke("my name is Erick and I am 27 years old") - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - assert response.content == "" - function_call = response.additional_kwargs.get("function_call") - assert function_call - assert function_call["name"] == "MyModel" - arguments_str = function_call.get("arguments") - assert arguments_str - arguments = json.loads(arguments_str) - assert arguments == { - "name": "Erick", - "age": 27.0, - } diff --git a/libs/partners/google-genai/tests/integration_tests/test_llms.py b/libs/partners/google-genai/tests/integration_tests/test_llms.py deleted file mode 100644 index b761e14804132..0000000000000 --- a/libs/partners/google-genai/tests/integration_tests/test_llms.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Test Google GenerativeAI API wrapper. - -Note: This test must be run with the GOOGLE_API_KEY environment variable set to a - valid API key. -""" - -from typing import Generator - -import pytest -from langchain_core.outputs import LLMResult - -from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory - -model_names = ["models/text-bison-001", "gemini-pro"] - - -@pytest.mark.parametrize( - "model_name", - model_names, -) -def test_google_generativeai_call(model_name: str) -> None: - """Test valid call to Google GenerativeAI text API.""" - if model_name: - llm = GoogleGenerativeAI(max_output_tokens=10, model=model_name) - else: - llm = GoogleGenerativeAI(max_output_tokens=10) - output = llm("Say foo:") - assert isinstance(output, str) - assert llm._llm_type == "google_palm" - if model_name and "gemini" in model_name: - assert llm.client.model_name == "models/gemini-pro" - else: - assert llm.model == "models/text-bison-001" - - -@pytest.mark.parametrize( - "model_name", - model_names, -) -def test_google_generativeai_generate(model_name: str) -> None: - n = 1 if model_name == "gemini-pro" else 2 - llm = GoogleGenerativeAI(temperature=0.3, n=n, model=model_name) - output = llm.generate(["Say foo:"]) - assert isinstance(output, LLMResult) - assert len(output.generations) == 1 - assert len(output.generations[0]) == n - - -def test_google_generativeai_get_num_tokens() -> None: - llm = GoogleGenerativeAI(model="models/text-bison-001") - output = llm.get_num_tokens("How are you?") - assert output == 4 - - -async def test_google_generativeai_agenerate() -> None: - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") - output = await llm.agenerate(["Please say foo:"]) - assert isinstance(output, LLMResult) - - -def test_generativeai_stream() -> None: - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") - outputs = list(llm.stream("Please say foo:")) - assert isinstance(outputs[0], str) - - -def test_generativeai_get_num_tokens_gemini() -> None: - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") - output = llm.get_num_tokens("How are you?") - assert output == 4 - - -def test_safety_settings_gemini() -> None: - # test with blocked prompt - llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") - output = llm.generate(prompts=["how to make a bomb?"]) - assert isinstance(output, LLMResult) - assert len(output.generations[0]) == 0 - - # safety filters - safety_settings = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, - } - - # test with safety filters directly to generate - output = llm.generate(["how to make a bomb?"], safety_settings=safety_settings) - assert isinstance(output, LLMResult) - assert len(output.generations[0]) > 0 - - # test with safety filters directly to stream - streamed_messages = [] - output_stream = llm.stream("how to make a bomb?", safety_settings=safety_settings) - assert isinstance(output_stream, Generator) - for message in output_stream: - streamed_messages.append(message) - assert len(streamed_messages) > 0 - - # test with safety filters on instantiation - llm = GoogleGenerativeAI( - model="gemini-pro", - safety_settings=safety_settings, - temperature=0, - ) - output = llm.generate(prompts=["how to make a bomb?"]) - assert isinstance(output, LLMResult) - assert len(output.generations[0]) > 0 diff --git a/libs/partners/google-genai/tests/unit_tests/__init__.py b/libs/partners/google-genai/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-genai/tests/unit_tests/test_chat_models.py b/libs/partners/google-genai/tests/unit_tests/test_chat_models.py deleted file mode 100644 index 61990909ed986..0000000000000 --- a/libs/partners/google-genai/tests/unit_tests/test_chat_models.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Test chat model integration.""" - -from typing import Dict, List, Union - -import pytest -from langchain_core.messages import ( - AIMessage, - FunctionMessage, - HumanMessage, - SystemMessage, -) -from langchain_core.pydantic_v1 import SecretStr -from pytest import CaptureFixture - -from langchain_google_genai.chat_models import ( - ChatGoogleGenerativeAI, - _parse_chat_history, -) - - -def test_integration_initialization() -> None: - """Test chat model initialization.""" - ChatGoogleGenerativeAI( - model="gemini-nano", - google_api_key="...", - top_k=2, - top_p=1, - temperature=0.7, - n=2, - ) - ChatGoogleGenerativeAI( - model="gemini-nano", - google_api_key="...", - top_k=2, - top_p=1, - temperature=0.7, - candidate_count=2, - ) - - -def test_api_key_is_string() -> None: - chat = ChatGoogleGenerativeAI(model="gemini-nano", google_api_key="secret-api-key") - assert isinstance(chat.google_api_key, SecretStr) - - -def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: - chat = ChatGoogleGenerativeAI(model="gemini-nano", google_api_key="secret-api-key") - print(chat.google_api_key, end="") # noqa: T201 - captured = capsys.readouterr() - - assert captured.out == "**********" - - -def test_parse_history() -> None: - system_input = "You're supposed to answer math questions." - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content=system_input) - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - messages = [system_message, message1, message2, message3] - history = _parse_chat_history(messages, convert_system_message_to_human=True) - assert len(history) == 3 - assert history[0] == { - "role": "user", - "parts": [{"text": system_input}, {"text": text_question1}], - } - assert history[1] == {"role": "model", "parts": [{"text": text_answer1}]} - - -@pytest.mark.parametrize("content", ['["a"]', '{"a":"b"}', "function output"]) -def test_parse_function_history(content: Union[str, List[Union[str, Dict]]]) -> None: - function_message = FunctionMessage(name="search_tool", content=content) - _parse_chat_history([function_message], convert_system_message_to_human=True) diff --git a/libs/partners/google-genai/tests/unit_tests/test_embeddings.py b/libs/partners/google-genai/tests/unit_tests/test_embeddings.py deleted file mode 100644 index 9bb57274e8993..0000000000000 --- a/libs/partners/google-genai/tests/unit_tests/test_embeddings.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Test embeddings model integration.""" - -from langchain_core.pydantic_v1 import SecretStr -from pytest import CaptureFixture - -from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings - - -def test_integration_initialization() -> None: - """Test chat model initialization.""" - GoogleGenerativeAIEmbeddings( - model="models/embedding-001", - google_api_key="...", - ) - GoogleGenerativeAIEmbeddings( - model="models/embedding-001", - google_api_key="...", - task_type="retrieval_document", - ) - - -def test_api_key_is_string() -> None: - embeddings = GoogleGenerativeAIEmbeddings( - model="models/embedding-001", - google_api_key="secret-api-key", - ) - assert isinstance(embeddings.google_api_key, SecretStr) - - -def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: - embeddings = GoogleGenerativeAIEmbeddings( - model="models/embedding-001", - google_api_key="secret-api-key", - ) - print(embeddings.google_api_key, end="") # noqa: T201 - captured = capsys.readouterr() - - assert captured.out == "**********" diff --git a/libs/partners/google-genai/tests/unit_tests/test_imports.py b/libs/partners/google-genai/tests/unit_tests/test_imports.py deleted file mode 100644 index 8c90cb2bd10d6..0000000000000 --- a/libs/partners/google-genai/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,13 +0,0 @@ -from langchain_google_genai import __all__ - -EXPECTED_ALL = [ - "ChatGoogleGenerativeAI", - "GoogleGenerativeAIEmbeddings", - "GoogleGenerativeAI", - "HarmBlockThreshold", - "HarmCategory", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/google-genai/tests/unit_tests/test_llms.py b/libs/partners/google-genai/tests/unit_tests/test_llms.py deleted file mode 100644 index 9b0ae77fc6188..0000000000000 --- a/libs/partners/google-genai/tests/unit_tests/test_llms.py +++ /dev/null @@ -1,8 +0,0 @@ -from langchain_google_genai.llms import GoogleModelFamily - - -def test_model_family() -> None: - model = GoogleModelFamily("gemini-pro") - assert model == GoogleModelFamily.GEMINI - model = GoogleModelFamily("gemini-ultra") - assert model == GoogleModelFamily.GEMINI diff --git a/libs/partners/google-vertexai/.gitignore b/libs/partners/google-vertexai/.gitignore deleted file mode 100644 index bee8a64b79a99..0000000000000 --- a/libs/partners/google-vertexai/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/libs/partners/google-vertexai/LICENSE b/libs/partners/google-vertexai/LICENSE deleted file mode 100644 index 426b65090341f..0000000000000 --- a/libs/partners/google-vertexai/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 LangChain, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/libs/partners/google-vertexai/Makefile b/libs/partners/google-vertexai/Makefile deleted file mode 100644 index 29214d4bbc70d..0000000000000 --- a/libs/partners/google-vertexai/Makefile +++ /dev/null @@ -1,61 +0,0 @@ -.PHONY: all format lint test tests integration_tests docker_tests help extended_tests - -# Default target executed when no arguments are given to make. -all: help - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ - -integration_tests: TEST_FILE = tests/integration_tests/ - -test integration_tests: - poetry run pytest $(TEST_FILE) - -tests: - poetry run pytest $(TEST_FILE) - - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/google-vertexai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_google_vertexai -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - poetry run ruff . - poetry run ruff format $(PYTHON_FILES) --diff - poetry run ruff --select I $(PYTHON_FILES) - mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) - -format format_diff: - poetry run ruff format $(PYTHON_FILES) - poetry run ruff --select I --fix $(PYTHON_FILES) - -spell_check: - poetry run codespell --toml pyproject.toml - -spell_fix: - poetry run codespell --toml pyproject.toml -w - -check_imports: $(shell find langchain_google_vertexai -name '*.py') - poetry run python ./scripts/check_imports.py $^ - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'check_imports - check imports' - @echo 'format - run code formatters' - @echo 'lint - run linters' - @echo 'test - run unit tests' - @echo 'tests - run unit tests' - @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/google-vertexai/README.md b/libs/partners/google-vertexai/README.md index 0637bd7cc9119..2ac1ce42e160a 100644 --- a/libs/partners/google-vertexai/README.md +++ b/libs/partners/google-vertexai/README.md @@ -1,100 +1,3 @@ -# langchain-google-vertexai +This package has moved! -This package contains the LangChain integrations for Google Cloud generative models. - -## Installation - -```bash -pip install -U langchain-google-vertexai -``` - -## Chat Models - -`ChatVertexAI` class exposes models such as `gemini-pro` and `chat-bison`. - -To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as: - -```python -from langchain_google_vertexai import ChatVertexAI - -llm = ChatVertexAI(model_name="gemini-pro") -llm.invoke("Sing a ballad of LangChain.") -``` - -You can use other models, e.g. `chat-bison`: -```python -from langchain_google_vertexai import ChatVertexAI - -llm = ChatVertexAI(model_name="chat-bison", temperature=0.3) -llm.invoke("Sing a ballad of LangChain.") -``` - -#### Multimodal inputs - -Gemini vision model supports image inputs when providing a single chat message. Example: - -```python -from langchain_core.messages import HumanMessage -from langchain_google_vertexai import ChatVertexAI - -llm = ChatVertexAI(model_name="gemini-pro-vision") -# example -message = HumanMessage( - content=[ - { - "type": "text", - "text": "What's in this image?", - }, # You can optionally provide text parts - {"type": "image_url", "image_url": {"url": "https://picsum.photos/seed/picsum/200/300"}}, - ] -) -llm.invoke([message]) -``` - -The value of `image_url` can be any of the following: - -- A public image URL -- An accessible gcs file (e.g., "gcs://path/to/file.png") -- A local file path -- A base64 encoded image (e.g., `data:image/png;base64,abcd124`) - - -## Embeddings - -You can use Google Cloud's embeddings models as: - -```python -from langchain_google_vertexai import VertexAIEmbeddings - -embeddings = VertexAIEmbeddings() -embeddings.embed_query("hello, world!") -``` - -## LLMs -You can use Google Cloud's generative AI models as Langchain LLMs: - -```python -from langchain.prompts import PromptTemplate -from langchain_google_vertexai import VertexAI - -template = """Question: {question} - -Answer: Let's think step by step.""" -prompt = PromptTemplate.from_template(template) - -chain = prompt | llm - -question = "Who was the president in the year Justin Beiber was born?" -print(chain.invoke({"question": question})) -``` - -You can use Gemini and Palm models, including code-generations ones: -```python -from langchain_google_vertexai import VertexAI - -llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3) - -question = "Write a python function that checks if a string is a valid email address" - -output = llm(question) -``` +https://github.com/langchain-ai/langchain-google/tree/main/libs/vertexai \ No newline at end of file diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py b/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py deleted file mode 100644 index be365bde4c33a..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from langchain_google_vertexai._enums import HarmBlockThreshold, HarmCategory -from langchain_google_vertexai.chains import create_structured_runnable -from langchain_google_vertexai.chat_models import ChatVertexAI -from langchain_google_vertexai.embeddings import VertexAIEmbeddings -from langchain_google_vertexai.functions_utils import PydanticFunctionsOutputParser -from langchain_google_vertexai.llms import VertexAI, VertexAIModelGarden - -__all__ = [ - "ChatVertexAI", - "VertexAIEmbeddings", - "VertexAI", - "VertexAIModelGarden", - "HarmBlockThreshold", - "HarmCategory", - "PydanticFunctionsOutputParser", - "create_structured_runnable", -] diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py b/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py deleted file mode 100644 index 00a2abaa32106..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/_enums.py +++ /dev/null @@ -1,6 +0,0 @@ -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 deleted file mode 100644 index 1af44f0c0f891..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Utilities to init Vertex AI.""" - -import dataclasses -from importlib import metadata -from typing import Any, Callable, Dict, Optional, Union - -import google.api_core -import proto # type: ignore[import-untyped] -from google.api_core.gapic_v1.client_info import ClientInfo -from google.cloud import storage # type: ignore[attr-defined] -from langchain_core.callbacks import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models.llms import create_base_retry_decorator -from vertexai.generative_models._generative_models import ( # type: ignore[import-untyped] - Candidate, -) -from vertexai.language_models import ( # type: ignore[import-untyped] - TextGenerationResponse, -) -from vertexai.preview.generative_models import Image # type: ignore[import-untyped] - - -def create_retry_decorator( - *, - max_retries: int = 1, - run_manager: Optional[ - Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] - ] = None, -) -> Callable[[Any], Any]: - """Creates a retry decorator for Vertex / Palm LLMs.""" - - errors = [ - google.api_core.exceptions.ResourceExhausted, - google.api_core.exceptions.ServiceUnavailable, - google.api_core.exceptions.Aborted, - google.api_core.exceptions.DeadlineExceeded, - google.api_core.exceptions.GoogleAPIError, - ] - decorator = create_base_retry_decorator( - error_types=errors, max_retries=max_retries, run_manager=run_manager - ) - return decorator - - -def raise_vertex_import_error(minimum_expected_version: str = "1.38.0") -> None: - """Raise ImportError related to Vertex SDK being not available. - - Args: - minimum_expected_version: The lowest expected version of the SDK. - Raises: - ImportError: an ImportError that mentions a required version of the SDK. - """ - raise ImportError( - "Please, install or upgrade the google-cloud-aiplatform library: " - f"pip install google-cloud-aiplatform>={minimum_expected_version}" - ) - - -def get_client_info(module: Optional[str] = None) -> "ClientInfo": - r"""Returns a custom user agent header. - - Args: - module (Optional[str]): - Optional. The module for a custom user agent header. - Returns: - google.api_core.gapic_v1.client_info.ClientInfo - """ - langchain_version = metadata.version("langchain") - client_library_version = ( - f"{langchain_version}-{module}" if module else langchain_version - ) - return ClientInfo( - client_library_version=client_library_version, - user_agent=f"langchain/{client_library_version}", - ) - - -def load_image_from_gcs(path: str, project: Optional[str] = None) -> Image: - """Loads im Image from GCS.""" - gcs_client = storage.Client(project=project) - pieces = path.split("/") - blobs = list(gcs_client.list_blobs(pieces[2], prefix="/".join(pieces[3:]))) - if len(blobs) > 1: - raise ValueError(f"Found more than one candidate for {path}!") - return Image.from_bytes(blobs[0].download_as_bytes()) - - -def is_codey_model(model_name: str) -> bool: - """Returns True if the model name is a Codey model.""" - return "code" in model_name - - -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: Union[TextGenerationResponse, Candidate], - is_gemini: bool, - *, - stream: bool = False, -) -> Dict[str, Any]: - if is_gemini: - # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini#response_body - info = { - "is_blocked": any([rating.blocked for rating in candidate.safety_ratings]), - "safety_ratings": [ - { - "category": rating.category.name, - "probability_label": rating.probability.name, - "blocked": rating.blocked, - } - for rating in candidate.safety_ratings - ], - "citation_metadata": ( - proto.Message.to_dict(candidate.citation_metadata) - if candidate.citation_metadata - else None - ), - } - # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-chat#response_body - else: - info = dataclasses.asdict(candidate) - info.pop("text") - info = {k: v for k, v in info.items() if not k.startswith("_")} - if stream: - # Remove non-streamable types, like bools. - info.pop("is_blocked") - return info diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/chains.py b/libs/partners/google-vertexai/langchain_google_vertexai/chains.py deleted file mode 100644 index 9b11794b30936..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/chains.py +++ /dev/null @@ -1,111 +0,0 @@ -from typing import ( - Dict, - Optional, - Sequence, - Type, - Union, -) - -from langchain_core.output_parsers import ( - BaseGenerationOutputParser, - BaseOutputParser, -) -from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import Runnable - -from langchain_google_vertexai.functions_utils import PydanticFunctionsOutputParser - - -def get_output_parser( - functions: Sequence[Type[BaseModel]], -) -> Union[BaseOutputParser, BaseGenerationOutputParser]: - """Get the appropriate function output parser given the user functions. - - Args: - functions: Sequence where element is a dictionary, a pydantic.BaseModel class, - or a Python function. If a dictionary is passed in, it is assumed to - already be a valid OpenAI function. - - Returns: - A PydanticFunctionsOutputParser - """ - function_names = [f.__name__ for f in functions] - if len(functions) > 1: - pydantic_schema: Union[Dict, Type[BaseModel]] = { - name: fn for name, fn in zip(function_names, functions) - } - else: - pydantic_schema = functions[0] - output_parser: Union[ - BaseOutputParser, BaseGenerationOutputParser - ] = PydanticFunctionsOutputParser(pydantic_schema=pydantic_schema) - return output_parser - - -def create_structured_runnable( - function: Union[Type[BaseModel], Sequence[Type[BaseModel]]], - llm: Runnable, - *, - prompt: Optional[BasePromptTemplate] = None, -) -> Runnable: - """Create a runnable sequence that uses OpenAI functions. - - Args: - function: Either a single pydantic.BaseModel class or a sequence - of pydantic.BaseModels classes. - For best results, pydantic.BaseModels - should have descriptions of the parameters. - llm: Language model to use, - assumed to support the Google Vertex function-calling API. - prompt: BasePromptTemplate to pass to the model. - - Returns: - A runnable sequence that will pass in the given functions to the model when run. - - Example: - .. code-block:: python - - from typing import Optional - - from langchain_google_vertexai import ChatVertexAI, create_structured_runnable - from langchain_core.prompts import ChatPromptTemplate - from langchain_core.pydantic_v1 import BaseModel, Field - - - class RecordPerson(BaseModel): - \"\"\"Record some identifying information about a person.\"\"\" - - name: str = Field(..., description="The person's name") - age: int = Field(..., description="The person's age") - fav_food: Optional[str] = Field(None, description="The person's favorite food") - - - class RecordDog(BaseModel): - \"\"\"Record some identifying information about a dog.\"\"\" - - name: str = Field(..., description="The dog's name") - color: str = Field(..., description="The dog's color") - fav_food: Optional[str] = Field(None, description="The dog's favorite food") - - - llm = ChatVertexAI(model_name="gemini-pro") - prompt = ChatPromptTemplate.from_template(\"\"\" - You are a world class algorithm for recording entities. - Make calls to the relevant function to record the entities in the following input: {input} - Tip: Make sure to answer in the correct format\"\"\" - ) - chain = create_structured_runnable([RecordPerson, RecordDog], llm, prompt=prompt) - chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) - # -> RecordDog(name="Harry", color="brown", fav_food="chicken") - """ # noqa: E501 - if not function: - raise ValueError("Need to pass in at least one function. Received zero.") - functions = function if isinstance(function, Sequence) else [function] - output_parser = get_output_parser(functions) - llm_with_functions = llm.bind(functions=functions) - if prompt is None: - initial_chain = llm_with_functions - else: - initial_chain = prompt | llm_with_functions - return initial_chain | output_parser diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py deleted file mode 100644 index 096f18006fc86..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py +++ /dev/null @@ -1,555 +0,0 @@ -"""Wrapper around Google VertexAI chat-based models.""" -from __future__ import annotations - -import base64 -import json -import logging -import re -from dataclasses import dataclass, field -from typing import Any, Dict, Iterator, List, Optional, Union, cast -from urllib.parse import urlparse - -import proto # type: ignore[import-untyped] -import requests -from google.cloud.aiplatform_v1beta1.types.content import Part as GapicPart -from google.cloud.aiplatform_v1beta1.types.tool import FunctionCall -from langchain_core.callbacks import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models.chat_models import ( - BaseChatModel, - generate_from_stream, -) -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, - FunctionMessage, - HumanMessage, - SystemMessage, -) -from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import root_validator -from vertexai.language_models import ( # type: ignore - ChatMessage, - ChatModel, - ChatSession, - CodeChatModel, - CodeChatSession, - InputOutputTextPair, -) -from vertexai.preview.generative_models import ( # type: ignore - Candidate, - Content, - GenerativeModel, - Image, - Part, -) -from vertexai.preview.language_models import ( # type: ignore - ChatModel as PreviewChatModel, -) -from vertexai.preview.language_models import ( - CodeChatModel as PreviewCodeChatModel, -) - -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 ( - _format_tools_to_vertex_tool, -) -from langchain_google_vertexai.llms import ( - _VertexAICommon, -) - -logger = logging.getLogger(__name__) - - -@dataclass -class _ChatHistory: - """Represents a context and a history of messages.""" - - history: List[ChatMessage] = field(default_factory=list) - context: Optional[str] = None - - -def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory: - """Parse a sequence of messages into history. - - Args: - history: The list of messages to re-create the history of the chat. - Returns: - A parsed chat history. - Raises: - ValueError: If a sequence of message has a SystemMessage not at the - first place. - """ - - vertex_messages, context = [], None - for i, message in enumerate(history): - content = cast(str, message.content) - if i == 0 and isinstance(message, SystemMessage): - context = content - elif isinstance(message, AIMessage): - vertex_message = ChatMessage(content=message.content, author="bot") - vertex_messages.append(vertex_message) - elif isinstance(message, HumanMessage): - vertex_message = ChatMessage(content=message.content, author="user") - vertex_messages.append(vertex_message) - else: - raise ValueError( - f"Unexpected message with type {type(message)} at the position {i}." - ) - chat_history = _ChatHistory(context=context, history=vertex_messages) - return chat_history - - -def _is_url(s: str) -> bool: - try: - result = urlparse(s) - return all([result.scheme, result.netloc]) - except Exception as e: - logger.debug(f"Unable to parse URL: {e}") - return False - - -def _parse_chat_history_gemini( - history: List[BaseMessage], - project: Optional[str] = None, - convert_system_message_to_human: Optional[bool] = False, -) -> List[Content]: - def _convert_to_prompt(part: Union[str, Dict]) -> Part: - if isinstance(part, str): - return Part.from_text(part) - - if not isinstance(part, Dict): - raise ValueError( - f"Message's content is expected to be a dict, got {type(part)}!" - ) - if part["type"] == "text": - return Part.from_text(part["text"]) - elif part["type"] == "image_url": - path = part["image_url"]["url"] - if path.startswith("gs://"): - image = load_image_from_gcs(path=path, project=project) - elif path.startswith("data:image/"): - # extract base64 component from image uri - try: - regexp = r"data:image/\w{2,4};base64,(.*)" - encoded = re.search(regexp, path).group(1) # type: ignore - except AttributeError: - raise ValueError( - "Invalid image uri. It should be in the format " - "data:image/;base64,." - ) - image = Image.from_bytes(base64.b64decode(encoded)) - elif _is_url(path): - response = requests.get(path) - response.raise_for_status() - image = Image.from_bytes(response.content) - else: - image = Image.load_from_file(path) - else: - raise ValueError("Only text and image_url types are supported!") - return Part.from_image(image) - - def _convert_to_parts(message: BaseMessage) -> List[Part]: - raw_content = message.content - if isinstance(raw_content, str): - raw_content = [raw_content] - return [_convert_to_prompt(part) for part in raw_content] - - vertex_messages = [] - raw_system_message = None - for i, message in enumerate(history): - if ( - i == 0 - and isinstance(message, SystemMessage) - and not convert_system_message_to_human - ): - raise ValueError( - """SystemMessages are not yet supported! - -To automatically convert the leading SystemMessage to a HumanMessage, -set `convert_system_message_to_human` to True. Example: - -llm = ChatVertexAI(model_name="gemini-pro", convert_system_message_to_human=True) -""" - ) - elif i == 0 and isinstance(message, SystemMessage): - raw_system_message = message - continue - elif isinstance(message, AIMessage): - raw_function_call = message.additional_kwargs.get("function_call") - role = "model" - if raw_function_call: - function_call = FunctionCall( - { - "name": raw_function_call["name"], - "args": json.loads(raw_function_call["arguments"]), - } - ) - gapic_part = GapicPart(function_call=function_call) - parts = [Part._from_gapic(gapic_part)] - else: - parts = _convert_to_parts(message) - elif isinstance(message, HumanMessage): - role = "user" - parts = _convert_to_parts(message) - elif isinstance(message, FunctionMessage): - role = "user" - parts = [ - Part.from_function_response( - name=message.name, - response={ - "content": message.content, - }, - ) - ] - else: - raise ValueError( - f"Unexpected message with type {type(message)} at the position {i}." - ) - - if raw_system_message: - if role == "model": - raise ValueError( - "SystemMessage should be followed by a HumanMessage and " - "not by AIMessage." - ) - parts = _convert_to_parts(raw_system_message) + parts - raw_system_message = None - - vertex_message = Content(role=role, parts=parts) - vertex_messages.append(vertex_message) - return vertex_messages - - -def _parse_examples(examples: List[BaseMessage]) -> List[InputOutputTextPair]: - if len(examples) % 2 != 0: - raise ValueError( - f"Expect examples to have an even amount of messages, got {len(examples)}." - ) - example_pairs = [] - input_text = None - for i, example in enumerate(examples): - if i % 2 == 0: - if not isinstance(example, HumanMessage): - raise ValueError( - f"Expected the first message in a part to be from human, got " - f"{type(example)} for the {i}th message." - ) - input_text = example.content - if i % 2 == 1: - if not isinstance(example, AIMessage): - raise ValueError( - f"Expected the second message in a part to be from AI, got " - f"{type(example)} for the {i}th message." - ) - pair = InputOutputTextPair( - input_text=input_text, output_text=example.content - ) - example_pairs.append(pair) - return example_pairs - - -def _get_question(messages: List[BaseMessage]) -> HumanMessage: - """Get the human message at the end of a list of input messages to a chat model.""" - if not messages: - raise ValueError("You should provide at least one message to start the chat!") - question = messages[-1] - if not isinstance(question, HumanMessage): - raise ValueError( - f"Last message in the list should be from human, got {question.type}." - ) - return question - - -def _parse_response_candidate(response_candidate: "Candidate") -> AIMessage: - try: - content = response_candidate.text - except ValueError: - content = "" - - additional_kwargs = {} - first_part = response_candidate.content.parts[0] - if first_part.function_call: - function_call = {"name": first_part.function_call.name} - # dump to match other function calling llm for now - function_call_args_dict = proto.Message.to_dict(first_part.function_call)[ - "args" - ] - function_call["arguments"] = json.dumps( - {k: function_call_args_dict[k] for k in function_call_args_dict} - ) - additional_kwargs["function_call"] = function_call - return AIMessage(content=content, additional_kwargs=additional_kwargs) - - -class ChatVertexAI(_VertexAICommon, BaseChatModel): - """`Vertex AI` Chat large language models API.""" - - model_name: str = "chat-bison" - "Underlying model name." - examples: Optional[List[BaseMessage]] = None - convert_system_message_to_human: bool = False - """Whether to merge any leading SystemMessage into the following HumanMessage. - - Gemini does not support system messages; any unsupported messages will - raise an error.""" - - @classmethod - def is_lc_serializable(self) -> bool: - return True - - @classmethod - def get_lc_namespace(cls) -> List[str]: - """Get the namespace of the langchain object.""" - return ["langchain", "chat_models", "vertexai"] - - @root_validator() - 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"], safety_settings=safety_settings - ) - values["client_preview"] = GenerativeModel( - model_name=values["model_name"], safety_settings=safety_settings - ) - else: - if is_codey_model(values["model_name"]): - model_cls = CodeChatModel - model_cls_preview = PreviewCodeChatModel - else: - model_cls = ChatModel - model_cls_preview = PreviewChatModel - values["client"] = model_cls.from_pretrained(values["model_name"]) - values["client_preview"] = model_cls_preview.from_pretrained( - values["model_name"] - ) - return values - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - stream: Optional[bool] = None, - **kwargs: Any, - ) -> ChatResult: - """Generate next turn in the conversation. - - Args: - messages: The history of the conversation as a list of messages. Code chat - does not support context. - stop: The list of stop words (optional). - run_manager: The CallbackManager for LLM run, it's not used at the moment. - stream: Whether to use the streaming endpoint. - - Returns: - The ChatResult that contains outputs generated by the model. - - Raises: - 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 - ) - return generate_from_stream(stream_iter) - - params = self._prepare_params(stop=stop, stream=False, **kwargs) - msg_params = {} - if "candidate_count" in params: - msg_params["candidate_count"] = params.pop("candidate_count") - - if self._is_gemini_model: - history_gemini = _parse_chat_history_gemini( - messages, - project=self.project, - convert_system_message_to_human=self.convert_system_message_to_human, - ) - message = history_gemini.pop() - chat = self.client.start_chat(history=history_gemini) - - # 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, - safety_settings=safety_settings, - ) - generations = [ - 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") or self.examples - if examples: - params["examples"] = _parse_examples(examples) - chat = self._start_chat(history, **params) - response = chat.send_message(question.content, **msg_params) - generations = [ - 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) - - async def _agenerate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - """Asynchronously generate next turn in the conversation. - - Args: - messages: The history of the conversation as a list of messages. Code chat - does not support context. - stop: The list of stop words (optional). - run_manager: The CallbackManager for LLM run, it's not used at the moment. - - Returns: - The ChatResult that contains outputs generated by the model. - - Raises: - ValueError: if the last message in the list is not from human. - """ - if "stream" in kwargs: - kwargs.pop("stream") - 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") - - if self._is_gemini_model: - history_gemini = _parse_chat_history_gemini( - messages, - project=self.project, - convert_system_message_to_human=self.convert_system_message_to_human, - ) - message = history_gemini.pop() - chat = self.client.start_chat(history=history_gemini) - # 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 = await chat.send_message_async( - message, - generation_config=params, - tools=tools, - safety_settings=safety_settings, - ) - generations = [ - 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) 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), - generation_info=get_generation_info(r, self._is_gemini_model), - ) - for r in response.candidates - ] - return ChatResult(generations=generations) - - def _stream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[ChatGenerationChunk]: - params = self._prepare_params(stop=stop, stream=True, **kwargs) - if self._is_gemini_model: - history_gemini = _parse_chat_history_gemini( - messages, - project=self.project, - convert_system_message_to_human=self.convert_system_message_to_human, - ) - message = history_gemini.pop() - chat = self.client.start_chat(history=history_gemini) - # 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 - safety_settings = params.pop("safety_settings", None) - responses = chat.send_message( - message, - stream=True, - generation_config=params, - safety_settings=safety_settings, - tools=tools, - ) - for response in responses: - message = _parse_response_candidate(response.candidates[0]) - if run_manager: - run_manager.on_llm_new_token(message.content) - yield ChatGenerationChunk( - message=AIMessageChunk( - content=message.content, - additional_kwargs=message.additional_kwargs, - ) - ) - else: - question = _get_question(messages) - history = _parse_chat_history(messages[:-1]) - examples = kwargs.get("examples", None) - if examples: - params["examples"] = _parse_examples(examples) - chat = self._start_chat(history, **params) - responses = chat.send_message_streaming(question.content, **params) - for response in responses: - if run_manager: - run_manager.on_llm_new_token(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 - ) -> Union[ChatSession, CodeChatSession]: - if not self.is_codey_model: - return self.client.start_chat( - context=history.context, message_history=history.history, **kwargs - ) - else: - return self.client.start_chat(message_history=history.history, **kwargs) diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py b/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py deleted file mode 100644 index 041c21fb92beb..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py +++ /dev/null @@ -1,336 +0,0 @@ -import logging -import re -import string -import threading -from concurrent.futures import ThreadPoolExecutor, wait -from typing import Any, Dict, List, Literal, Optional, Tuple, Type - -from google.api_core.exceptions import ( - Aborted, - DeadlineExceeded, - InvalidArgument, - ResourceExhausted, - ServiceUnavailable, -) -from langchain_core.embeddings import Embeddings -from langchain_core.language_models.llms import create_base_retry_decorator -from langchain_core.pydantic_v1 import root_validator -from vertexai.language_models import ( # type: ignore - TextEmbeddingInput, - TextEmbeddingModel, -) - -from langchain_google_vertexai.llms import _VertexAICommon - -logger = logging.getLogger(__name__) - -_MAX_TOKENS_PER_BATCH = 20000 -_MAX_BATCH_SIZE = 250 -_MIN_BATCH_SIZE = 5 - - -class VertexAIEmbeddings(_VertexAICommon, Embeddings): - """Google Cloud VertexAI embedding models.""" - - # Instance context - instance: Dict[str, Any] = {} #: :meta private: - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validates that the python package exists in environment.""" - cls._init_vertexai(values) - if values["model_name"] == "textembedding-gecko-default": - logger.warning( - "Model_name will become a required arg for VertexAIEmbeddings " - "starting from Feb-01-2024. Currently the default is set to " - "textembedding-gecko@001" - ) - values["model_name"] = "textembedding-gecko@001" - values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"]) - return values - - def __init__( - self, - # the default value would be removed after Feb-01-2024 - model_name: str = "textembedding-gecko-default", - project: Optional[str] = None, - location: str = "us-central1", - request_parallelism: int = 5, - max_retries: int = 6, - credentials: Optional[Any] = None, - **kwargs: Any, - ): - """Initialize the sentence_transformer.""" - super().__init__( - project=project, - location=location, - credentials=credentials, - request_parallelism=request_parallelism, - max_retries=max_retries, - model_name=model_name, - **kwargs, - ) - self.instance["max_batch_size"] = kwargs.get("max_batch_size", _MAX_BATCH_SIZE) - self.instance["batch_size"] = self.instance["max_batch_size"] - self.instance["min_batch_size"] = kwargs.get("min_batch_size", _MIN_BATCH_SIZE) - self.instance["min_good_batch_size"] = self.instance["min_batch_size"] - self.instance["lock"] = threading.Lock() - self.instance["batch_size_validated"] = False - self.instance["task_executor"] = ThreadPoolExecutor( - max_workers=request_parallelism - ) - self.instance[ - "embeddings_task_type_supported" - ] = not self.client._endpoint_name.endswith("/textembedding-gecko@001") - - @staticmethod - def _split_by_punctuation(text: str) -> List[str]: - """Splits a string by punctuation and whitespace characters.""" - split_by = string.punctuation + "\t\n " - pattern = f"([{split_by}])" - # Using re.split to split the text based on the pattern - return [segment for segment in re.split(pattern, text) if segment] - - @staticmethod - def _prepare_batches(texts: List[str], batch_size: int) -> List[List[str]]: - """Splits texts in batches based on current maximum batch size - and maximum tokens per request. - """ - text_index = 0 - texts_len = len(texts) - batch_token_len = 0 - batches: List[List[str]] = [] - current_batch: List[str] = [] - if texts_len == 0: - return [] - while text_index < texts_len: - current_text = texts[text_index] - # Number of tokens per a text is conservatively estimated - # as 2 times number of words, punctuation and whitespace characters. - # Using `count_tokens` API will make batching too expensive. - # Utilizing a tokenizer, would add a dependency that would not - # necessarily be reused by the application using this class. - current_text_token_cnt = ( - len(VertexAIEmbeddings._split_by_punctuation(current_text)) * 2 - ) - end_of_batch = False - if current_text_token_cnt > _MAX_TOKENS_PER_BATCH: - # Current text is too big even for a single batch. - # Such request will fail, but we still make a batch - # so that the app can get the error from the API. - if len(current_batch) > 0: - # Adding current batch if not empty. - batches.append(current_batch) - current_batch = [current_text] - text_index += 1 - end_of_batch = True - elif ( - batch_token_len + current_text_token_cnt > _MAX_TOKENS_PER_BATCH - or len(current_batch) == batch_size - ): - end_of_batch = True - else: - if text_index == texts_len - 1: - # Last element - even though the batch may be not big, - # we still need to make it. - end_of_batch = True - batch_token_len += current_text_token_cnt - current_batch.append(current_text) - text_index += 1 - if end_of_batch: - batches.append(current_batch) - current_batch = [] - batch_token_len = 0 - return batches - - def _get_embeddings_with_retry( - self, texts: List[str], embeddings_type: Optional[str] = None - ) -> List[List[float]]: - """Makes a Vertex AI model request with retry logic.""" - - errors: List[Type[BaseException]] = [ - ResourceExhausted, - ServiceUnavailable, - Aborted, - DeadlineExceeded, - ] - retry_decorator = create_base_retry_decorator( - error_types=errors, max_retries=self.max_retries - ) - - @retry_decorator - def _completion_with_retry(texts_to_process: List[str]) -> Any: - if embeddings_type and self.instance["embeddings_task_type_supported"]: - requests = [ - TextEmbeddingInput(text=t, task_type=embeddings_type) - for t in texts_to_process - ] - else: - requests = texts_to_process - embeddings = self.client.get_embeddings(requests) - return [embs.values for embs in embeddings] - - return _completion_with_retry(texts) - - def _prepare_and_validate_batches( - self, texts: List[str], embeddings_type: Optional[str] = None - ) -> Tuple[List[List[float]], List[List[str]]]: - """Prepares text batches with one-time validation of batch size. - Batch size varies between GCP regions and individual project quotas. - # Returns embeddings of the first text batch that went through, - # and text batches for the rest of the texts. - """ - - batches = VertexAIEmbeddings._prepare_batches( - texts, self.instance["batch_size"] - ) - # If batch size if less or equal to one that went through before, - # then keep batches as they are. - if len(batches[0]) <= self.instance["min_good_batch_size"]: - return [], batches - with self.instance["lock"]: - # If largest possible batch size was validated - # while waiting for the lock, then check for rebuilding - # our batches, and return. - if self.instance["batch_size_validated"]: - if len(batches[0]) <= self.instance["batch_size"]: - return [], batches - else: - return [], VertexAIEmbeddings._prepare_batches( - texts, self.instance["batch_size"] - ) - # Figure out largest possible batch size by trying to push - # batches and lowering their size in half after every failure. - first_batch = batches[0] - first_result = [] - had_failure = False - while True: - try: - first_result = self._get_embeddings_with_retry( - first_batch, embeddings_type - ) - break - except InvalidArgument: - had_failure = True - first_batch_len = len(first_batch) - if first_batch_len == self.instance["min_batch_size"]: - raise - first_batch_len = max( - self.instance["min_batch_size"], int(first_batch_len / 2) - ) - first_batch = first_batch[:first_batch_len] - first_batch_len = len(first_batch) - self.instance["min_good_batch_size"] = max( - self.instance["min_good_batch_size"], first_batch_len - ) - # If had a failure and recovered - # or went through with the max size, then it's a legit batch size. - if had_failure or first_batch_len == self.instance["max_batch_size"]: - self.instance["batch_size"] = first_batch_len - self.instance["batch_size_validated"] = True - # If batch size was updated, - # rebuild batches with the new batch size - # (texts that went through are excluded here). - if first_batch_len != self.instance["max_batch_size"]: - batches = VertexAIEmbeddings._prepare_batches( - texts[first_batch_len:], self.instance["batch_size"] - ) - else: - # Still figuring out max batch size. - batches = batches[1:] - # Returning embeddings of the first text batch that went through, - # and text batches for the rest of texts. - return first_result, batches - - def embed( - self, - texts: List[str], - batch_size: int = 0, - embeddings_task_type: Optional[ - Literal[ - "RETRIEVAL_QUERY", - "RETRIEVAL_DOCUMENT", - "SEMANTIC_SIMILARITY", - "CLASSIFICATION", - "CLUSTERING", - ] - ] = None, - ) -> List[List[float]]: - """Embed a list of strings. - - Args: - texts: List[str] The list of strings to embed. - batch_size: [int] The batch size of embeddings to send to the model. - If zero, then the largest batch size will be detected dynamically - at the first request, starting from 250, down to 5. - embeddings_task_type: [str] optional embeddings task type, - one of the following - RETRIEVAL_QUERY - Text is a query - in a search/retrieval setting. - RETRIEVAL_DOCUMENT - Text is a document - in a search/retrieval setting. - SEMANTIC_SIMILARITY - Embeddings will be used - for Semantic Textual Similarity (STS). - CLASSIFICATION - Embeddings will be used for classification. - CLUSTERING - Embeddings will be used for clustering. - - Returns: - List of embeddings, one for each text. - """ - if len(texts) == 0: - return [] - embeddings: List[List[float]] = [] - first_batch_result: List[List[float]] = [] - if batch_size > 0: - # Fixed batch size. - batches = VertexAIEmbeddings._prepare_batches(texts, batch_size) - else: - # Dynamic batch size, starting from 250 at the first call. - first_batch_result, batches = self._prepare_and_validate_batches( - texts, embeddings_task_type - ) - # First batch result may have some embeddings already. - # In such case, batches have texts that were not processed yet. - embeddings.extend(first_batch_result) - tasks = [] - for batch in batches: - tasks.append( - self.instance["task_executor"].submit( - self._get_embeddings_with_retry, - texts=batch, - embeddings_type=embeddings_task_type, - ) - ) - if len(tasks) > 0: - wait(tasks) - for t in tasks: - embeddings.extend(t.result()) - return embeddings - - def embed_documents( - self, texts: List[str], batch_size: int = 0 - ) -> List[List[float]]: - """Embed a list of documents. - - Args: - texts: List[str] The list of texts to embed. - batch_size: [int] The batch size of embeddings to send to the model. - If zero, then the largest batch size will be detected dynamically - at the first request, starting from 250, down to 5. - - Returns: - List of embeddings, one for each text. - """ - return self.embed(texts, batch_size, "RETRIEVAL_DOCUMENT") - - def embed_query(self, text: str) -> List[float]: - """Embed a text. - - Args: - text: The text to embed. - - Returns: - Embedding for the text. - """ - embeddings = self.embed([text], 1, "RETRIEVAL_QUERY") - return embeddings[0] diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/functions_utils.py b/libs/partners/google-vertexai/langchain_google_vertexai/functions_utils.py deleted file mode 100644 index 1ae43dffcdf06..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/functions_utils.py +++ /dev/null @@ -1,152 +0,0 @@ -import json -from typing import Dict, List, Type, Union - -from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers import BaseOutputParser -from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.tools import BaseTool -from langchain_core.utils.function_calling import FunctionDescription -from langchain_core.utils.json_schema import dereference_refs -from vertexai.preview.generative_models import ( # type: ignore - FunctionDeclaration, -) -from vertexai.preview.generative_models import Tool as VertexTool - - -def _format_pydantic_to_vertex_function( - pydantic_model: Type[BaseModel], -) -> FunctionDescription: - schema = dereference_refs(pydantic_model.schema()) - schema.pop("definitions", None) - - return { - "name": schema["title"], - "description": schema.get("description", ""), - "parameters": { - "properties": { - k: { - "type": v["type"], - "description": v.get("description"), - } - for k, v in schema["properties"].items() - }, - "required": schema["required"], - "type": schema["type"], - }, - } - - -def _format_tool_to_vertex_function(tool: BaseTool) -> FunctionDescription: - "Format tool into the Vertex function API." - if tool.args_schema: - schema = dereference_refs(tool.args_schema.schema()) - schema.pop("definitions", None) - - return { - "name": tool.name or schema["title"], - "description": tool.description or schema["description"], - "parameters": { - "properties": { - k: { - "type": v["type"], - "description": v.get("description"), - } - for k, v in schema["properties"].items() - }, - "required": schema["required"], - "type": schema["type"], - }, - } - else: - return { - "name": tool.name, - "description": tool.description, - "parameters": { - "properties": { - "__arg1": {"type": "string"}, - }, - "required": ["__arg1"], - "type": "object", - }, - } - - -def _format_tools_to_vertex_tool( - tools: List[Union[BaseTool, Type[BaseModel]]], -) -> List[VertexTool]: - "Format tool into the Vertex Tool instance." - function_declarations = [] - for tool in tools: - if isinstance(tool, BaseTool): - func = _format_tool_to_vertex_function(tool) - else: - func = _format_pydantic_to_vertex_function(tool) - function_declarations.append(FunctionDeclaration(**func)) - - return [VertexTool(function_declarations=function_declarations)] - - -class PydanticFunctionsOutputParser(BaseOutputParser): - """Parse an output as a pydantic object. - - This parser is used to parse the output of a ChatModel that uses - Google Vertex function format to invoke functions. - - The parser extracts the function call invocation and matches - them to the pydantic schema provided. - - An exception will be raised if the function call does not match - the provided schema. - - Example: - - ... code-block:: python - - message = AIMessage( - content="This is a test message", - additional_kwargs={ - "function_call": { - "name": "cookie", - "arguments": json.dumps({"name": "value", "age": 10}), - } - }, - ) - chat_generation = ChatGeneration(message=message) - - class Cookie(BaseModel): - name: str - age: int - - class Dog(BaseModel): - species: str - - # Full output - parser = PydanticOutputFunctionsParser( - pydantic_schema={"cookie": Cookie, "dog": Dog} - ) - result = parser.parse_result([chat_generation]) - """ - - pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]] - - def parse_result( - self, result: List[Generation], *, partial: bool = False - ) -> BaseModel: - if not isinstance(result[0], ChatGeneration): - raise ValueError("This output parser only works on ChatGeneration output") - message = result[0].message - function_call = message.additional_kwargs.get("function_call", {}) - if function_call: - function_name = function_call["name"] - tool_input = function_call.get("arguments", {}) - if isinstance(self.pydantic_schema, dict): - schema = self.pydantic_schema[function_name] - else: - schema = self.pydantic_schema - return schema(**json.loads(tool_input)) - else: - raise OutputParserException(f"Could not parse function call: {message}") - - def parse(self, text: str) -> BaseModel: - raise ValueError("Can only parse messages") diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py deleted file mode 100644 index 9b3099b4c3321..0000000000000 --- a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py +++ /dev/null @@ -1,555 +0,0 @@ -from __future__ import annotations - -from concurrent.futures import Executor -from typing import Any, ClassVar, Dict, Iterator, List, Optional, Union - -import vertexai # type: ignore[import-untyped] -from google.api_core.client_options import ClientOptions -from google.cloud.aiplatform.gapic import ( - PredictionServiceAsyncClient, - PredictionServiceClient, -) -from google.cloud.aiplatform.models import Prediction -from google.protobuf import json_format -from google.protobuf.struct_pb2 import Value -from langchain_core.callbacks.manager import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models.llms import BaseLLM -from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator -from vertexai.language_models import ( # type: ignore[import-untyped] - CodeGenerationModel, - TextGenerationModel, -) -from vertexai.language_models._language_models import ( # type: ignore[import-untyped] - TextGenerationResponse, -) -from vertexai.preview.generative_models import ( # type: ignore[import-untyped] - GenerativeModel, - Image, -) -from vertexai.preview.language_models import ( # type: ignore[import-untyped] - ChatModel as PreviewChatModel, -) -from vertexai.preview.language_models import ( - CodeChatModel as PreviewCodeChatModel, -) -from vertexai.preview.language_models import ( - CodeGenerationModel as PreviewCodeGenerationModel, -) -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, -) - -_PALM_DEFAULT_MAX_OUTPUT_TOKENS = TextGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS -_PALM_DEFAULT_TEMPERATURE = 0.0 -_PALM_DEFAULT_TOP_P = 0.95 -_PALM_DEFAULT_TOP_K = 40 - - -def _completion_with_retry( - llm: VertexAI, - prompt: List[Union[str, Image]], - stream: bool = False, - is_gemini: bool = False, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, -) -> Any: - """Use tenacity to retry the completion call.""" - retry_decorator = create_retry_decorator( - max_retries=llm.max_retries, run_manager=run_manager - ) - - @retry_decorator - def _completion_with_retry_inner( - prompt: List[Union[str, Image]], is_gemini: bool = False, **kwargs: Any - ) -> Any: - if is_gemini: - return llm.client.generate_content( - prompt, - stream=stream, - safety_settings=kwargs.pop("safety_settings", None), - generation_config=kwargs, - ) - else: - if stream: - return llm.client.predict_streaming(prompt[0], **kwargs) - return llm.client.predict(prompt[0], **kwargs) - - return _completion_with_retry_inner(prompt, is_gemini, **kwargs) - - -async def _acompletion_with_retry( - llm: VertexAI, - prompt: str, - is_gemini: bool = False, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, -) -> Any: - """Use tenacity to retry the completion call.""" - retry_decorator = create_retry_decorator( - max_retries=llm.max_retries, run_manager=run_manager - ) - - @retry_decorator - async def _acompletion_with_retry_inner( - prompt: str, is_gemini: bool = False, **kwargs: Any - ) -> Any: - if is_gemini: - return await llm.client.generate_content_async( - prompt, - generation_config=kwargs, - safety_settings=kwargs.pop("safety_settings", None), - ) - return await llm.client.predict_async(prompt, **kwargs) - - return await _acompletion_with_retry_inner(prompt, is_gemini, **kwargs) - - -class _VertexAIBase(BaseModel): - project: Optional[str] = None - "The default GCP project to use when making Vertex API calls." - location: str = "us-central1" - "The default location to use when making API calls." - request_parallelism: int = 5 - "The amount of parallelism allowed for requests issued to VertexAI models. " - "Default is 5." - max_retries: int = 6 - """The maximum number of retries to make when generating.""" - task_executor: ClassVar[Optional[Executor]] = Field(default=None, exclude=True) - stop: Optional[List[str]] = None - "Optional list of stop words to use when generating." - model_name: Optional[str] = None - "Underlying model name." - - -class _VertexAICommon(_VertexAIBase): - client: Any = None #: :meta private: - client_preview: Any = None #: :meta private: - model_name: str - "Underlying model name." - temperature: Optional[float] = None - "Sampling temperature, it controls the degree of randomness in token selection." - max_output_tokens: Optional[int] = None - "Token limit determines the maximum amount of text output from one prompt." - top_p: Optional[float] = None - "Tokens are selected from most probable to least until the sum of their " - "probabilities equals the top-p value. Top-p is ignored for Codey models." - top_k: Optional[int] = None - "How the model selects tokens for output, the next token is selected from " - "among the top-k most probable tokens. Top-k is ignored for Codey models." - credentials: Any = Field(default=None, exclude=True) - "The default custom credentials (google.auth.credentials.Credentials) to use " - "when making API calls. If not provided, credentials will be ascertained from " - "the environment." - n: int = 1 - """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: - return "vertexai" - - @property - def is_codey_model(self) -> bool: - return is_codey_model(self.model_name) - - @property - def _is_gemini_model(self) -> bool: - return is_gemini_model(self.model_name) - - @property - def _identifying_params(self) -> Dict[str, Any]: - """Gets the identifying parameters.""" - return {**{"model_name": self.model_name}, **self._default_params} - - @property - def _default_params(self) -> Dict[str, Any]: - if self._is_gemini_model: - default_params = {} - else: - default_params = { - "temperature": _PALM_DEFAULT_TEMPERATURE, - "max_output_tokens": _PALM_DEFAULT_MAX_OUTPUT_TOKENS, - "top_p": _PALM_DEFAULT_TOP_P, - "top_k": _PALM_DEFAULT_TOP_K, - } - params = { - "temperature": self.temperature, - "max_output_tokens": self.max_output_tokens, - "candidate_count": self.n, - } - if not self.is_codey_model: - params.update( - { - "top_k": self.top_k, - "top_p": self.top_p, - } - ) - updated_params = {} - for param_name, param_value in params.items(): - default_value = default_params.get(param_name) - if param_value or default_value: - updated_params[param_name] = ( - param_value if param_value else default_value - ) - return updated_params - - @classmethod - def _init_vertexai(cls, values: Dict) -> None: - vertexai.init( - project=values.get("project"), - location=values.get("location"), - credentials=values.get("credentials"), - ) - return None - - def _prepare_params( - self, - stop: Optional[List[str]] = None, - stream: bool = False, - **kwargs: Any, - ) -> dict: - stop_sequences = stop or self.stop - params_mapping = {"n": "candidate_count"} - params = {params_mapping.get(k, k): v for k, v in kwargs.items()} - params = {**self._default_params, "stop_sequences": stop_sequences, **params} - if stream or self.streaming: - params.pop("candidate_count") - return params - - def get_num_tokens(self, text: str) -> int: - """Get the number of tokens present in the text. - - Useful for checking if an input will fit in a model's context window. - - Args: - text: The string input to tokenize. - - Returns: - The integer number of tokens in the text. - """ - is_palm_chat_model = isinstance( - self.client_preview, PreviewChatModel - ) or isinstance(self.client_preview, PreviewCodeChatModel) - if is_palm_chat_model: - result = self.client_preview.start_chat().count_tokens(text) - else: - result = self.client_preview.count_tokens([text]) - - return result.total_tokens - - -class VertexAI(_VertexAICommon, BaseLLM): - """Google Vertex AI large language models.""" - - model_name: str = "text-bison" - "The name of the Vertex AI large language model." - tuned_model_name: Optional[str] = None - "The name of a tuned model. If provided, model_name is ignored." - - @classmethod - def is_lc_serializable(self) -> bool: - return True - - @classmethod - def get_lc_namespace(cls) -> List[str]: - """Get the namespace of the langchain object.""" - return ["langchain", "llms", "vertexai"] - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """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 - elif is_gemini: - model_cls = GenerativeModel - preview_model_cls = GenerativeModel - else: - model_cls = TextGenerationModel - preview_model_cls = PreviewTextGenerationModel - - if tuned_model_name: - values["client"] = model_cls.get_tuned_model(tuned_model_name) - values["client_preview"] = preview_model_cls.get_tuned_model( - tuned_model_name - ) - else: - if is_gemini: - 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) - - if values["streaming"] and values["n"] > 1: - raise ValueError("Only one candidate can be generated with streaming!") - return values - - def _response_to_generation( - self, response: TextGenerationResponse, *, stream: bool = False - ) -> GenerationChunk: - """Converts a stream response to a generation chunk.""" - generation_info = get_generation_info( - response, self._is_gemini_model, stream=stream - ) - try: - text = response.text - except AttributeError: - text = "" - except ValueError: - text = "" - return GenerationChunk( - text=text, - generation_info=generation_info, - ) - - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - stream: Optional[bool] = None, - **kwargs: Any, - ) -> LLMResult: - should_stream = stream if stream is not None else self.streaming - params = self._prepare_params(stop=stop, stream=should_stream, **kwargs) - generations: List[List[Generation]] = [] - for prompt in prompts: - if should_stream: - generation = GenerationChunk(text="") - for chunk in self._stream( - prompt, stop=stop, run_manager=run_manager, **kwargs - ): - generation += chunk - generations.append([generation]) - else: - res = _completion_with_retry( - self, - [prompt], - stream=should_stream, - is_gemini=self._is_gemini_model, - run_manager=run_manager, - **params, - ) - generations.append( - [self._response_to_generation(r) for r in res.candidates] - ) - return LLMResult(generations=generations) - - async def _agenerate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - params = self._prepare_params(stop=stop, **kwargs) - generations: List[List[Generation]] = [] - for prompt in prompts: - res = await _acompletion_with_retry( - self, - prompt, - is_gemini=self._is_gemini_model, - run_manager=run_manager, - **params, - ) - generations.append( - [self._response_to_generation(r) for r in res.candidates] - ) - return LLMResult(generations=generations) - - def _stream( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[GenerationChunk]: - params = self._prepare_params(stop=stop, stream=True, **kwargs) - for stream_resp in _completion_with_retry( - self, - [prompt], - stream=True, - is_gemini=self._is_gemini_model, - run_manager=run_manager, - **params, - ): - # Gemini models return GenerationResponse even when streaming, which has a - # candidates field. - stream_resp = ( - stream_resp - if isinstance(stream_resp, TextGenerationResponse) - else stream_resp.candidates[0] - ) - chunk = self._response_to_generation(stream_resp, stream=True) - yield chunk - if run_manager: - run_manager.on_llm_new_token( - chunk.text, - chunk=chunk, - verbose=self.verbose, - ) - - -class VertexAIModelGarden(_VertexAIBase, BaseLLM): - """Large language models served from Vertex AI Model Garden.""" - - client: Any = None #: :meta private: - async_client: Any = None #: :meta private: - endpoint_id: str - "A name of an endpoint where the model has been deployed." - allowed_model_args: Optional[List[str]] = None - "Allowed optional args to be passed to the model." - prompt_arg: str = "prompt" - result_arg: Optional[str] = "generated_text" - "Set result_arg to None if output of the model is expected to be a string." - "Otherwise, if it's a dict, provided an argument that contains the result." - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validate that the python package exists in environment.""" - - if not values["project"]: - raise ValueError( - "A GCP project should be provided to run inference on Model Garden!" - ) - - client_options = ClientOptions( - api_endpoint=f"{values['location']}-aiplatform.googleapis.com" - ) - client_info = get_client_info(module="vertex-ai-model-garden") - values["client"] = PredictionServiceClient( - client_options=client_options, client_info=client_info - ) - values["async_client"] = PredictionServiceAsyncClient( - client_options=client_options, client_info=client_info - ) - return values - - @property - def endpoint_path(self) -> str: - return self.client.endpoint_path( - project=self.project, location=self.location, endpoint=self.endpoint_id - ) - - @property - def _llm_type(self) -> str: - return "vertexai_model_garden" - - def _prepare_request(self, prompts: List[str], **kwargs: Any) -> List["Value"]: - instances = [] - for prompt in prompts: - if self.allowed_model_args: - instance = { - k: v for k, v in kwargs.items() if k in self.allowed_model_args - } - else: - instance = {} - instance[self.prompt_arg] = prompt - instances.append(instance) - - predict_instances = [ - json_format.ParseDict(instance_dict, Value()) for instance_dict in instances - ] - return predict_instances - - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - """Run the LLM on the given prompt and input.""" - instances = self._prepare_request(prompts, **kwargs) - response = self.client.predict(endpoint=self.endpoint_path, instances=instances) - return self._parse_response(response) - - def _parse_response(self, predictions: "Prediction") -> LLMResult: - generations: List[List[Generation]] = [] - for result in predictions.predictions: - generations.append( - [ - Generation(text=self._parse_prediction(prediction)) - for prediction in result - ] - ) - return LLMResult(generations=generations) - - def _parse_prediction(self, prediction: Any) -> str: - if isinstance(prediction, str): - return prediction - - if self.result_arg: - try: - return prediction[self.result_arg] - except KeyError: - if isinstance(prediction, str): - error_desc = ( - "Provided non-None `result_arg` (result_arg=" - f"{self.result_arg}). But got prediction of type " - f"{type(prediction)} instead of dict. Most probably, you" - "need to set `result_arg=None` during VertexAIModelGarden " - "initialization." - ) - raise ValueError(error_desc) - else: - raise ValueError(f"{self.result_arg} key not found in prediction!") - - return prediction - - async def _agenerate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - """Run the LLM on the given prompt and input.""" - instances = self._prepare_request(prompts, **kwargs) - response = await self.async_client.predict( - endpoint=self.endpoint_path, instances=instances - ) - return self._parse_response(response) diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/py.typed b/libs/partners/google-vertexai/langchain_google_vertexai/py.typed deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-vertexai/poetry.lock b/libs/partners/google-vertexai/poetry.lock deleted file mode 100644 index 794515251cdec..0000000000000 --- a/libs/partners/google-vertexai/poetry.lock +++ /dev/null @@ -1,2261 +0,0 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - 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-[tool.poetry.group.test] -optional = true - -[tool.poetry.group.test.dependencies] -pytest = "^7.3.0" -freezegun = "^1.2.2" -pytest-mock = "^3.10.0" -syrupy = "^4.0.2" -pytest-watcher = "^0.3.4" -pytest-asyncio = "^0.21.1" -langchain-core = { path = "../../core", develop = true } - -[tool.poetry.group.codespell] -optional = true - -[tool.poetry.group.codespell.dependencies] -codespell = "^2.2.0" - -[tool.poetry.group.test_integration] -optional = true - -[tool.poetry.group.test_integration.dependencies] -langchain = { path = "../../langchain" } -langchain-community = { path = "../../community" } -numexpr = { version = "^2.8.8", python = ">=3.9,<4.0" } -google-api-python-client = "^2.114.0" - -[tool.poetry.group.lint] -optional = true - -[tool.poetry.group.lint.dependencies] -ruff = "^0.1.5" - -[tool.poetry.group.typing.dependencies] -mypy = "^1" -langchain-core = { path = "../../core", develop = true } -types-google-cloud-ndb = "^2.2.0.20240106" -types-requests = "^2.31.0.20231231" -types-protobuf = "^4.24.0.4" - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.dev.dependencies] -langchain-core = { path = "../../core", develop = true } - -[tool.ruff.lint] -select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort - "T201", # print -] - -[tool.mypy] -check_untyped_defs = true -error_summary = false -pretty = true -show_column_numbers = true -show_error_codes = true -show_error_context = true -warn_redundant_casts = true -warn_unreachable = true -warn_unused_configs = true -warn_unused_ignores = true - -[tool.coverage.run] -omit = ["tests/*"] - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" - -[tool.pytest.ini_options] -# --strict-markers will raise errors on unknown marks. -# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks -# -# https://docs.pytest.org/en/7.1.x/reference/reference.html -# --strict-config any warnings encountered while parsing the `pytest` -# section of the configuration file raise errors. -# -# https://github.com/tophat/syrupy -# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. -addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -# Registering custom markers. -# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers -markers = [ - "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", - "compile: mark placeholder test used to compile integration tests without running them", -] -asyncio_mode = "auto" diff --git a/libs/partners/google-vertexai/scripts/check_imports.py b/libs/partners/google-vertexai/scripts/check_imports.py deleted file mode 100644 index 365f5fa118da4..0000000000000 --- a/libs/partners/google-vertexai/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -import sys -import traceback -from importlib.machinery import SourceFileLoader - -if __name__ == "__main__": - files = sys.argv[1:] - has_failure = False - for file in files: - try: - SourceFileLoader("x", file).load_module() - except Exception: - has_faillure = True - print(file) # noqa: T201 - traceback.print_exc() - print() # noqa: T201 - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/google-vertexai/scripts/check_pydantic.sh b/libs/partners/google-vertexai/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/google-vertexai/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/google-vertexai/scripts/lint_imports.sh b/libs/partners/google-vertexai/scripts/lint_imports.sh deleted file mode 100755 index 695613c7ba8fd..0000000000000 --- a/libs/partners/google-vertexai/scripts/lint_imports.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -eu - -# Initialize a variable to keep track of errors -errors=0 - -# make sure not importing from langchain or langchain_experimental -git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) - -# Decide on an exit status based on the errors -if [ "$errors" -gt 0 ]; then - exit 1 -else - exit 0 -fi diff --git a/libs/partners/google-vertexai/tests/__init__.py b/libs/partners/google-vertexai/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-vertexai/tests/integration_tests/__init__.py b/libs/partners/google-vertexai/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py deleted file mode 100644 index 338533103ea9b..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py +++ /dev/null @@ -1,260 +0,0 @@ -"""Test ChatGoogleVertexAI chat model.""" - -import json -from typing import Optional, cast - -import pytest -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - HumanMessage, - SystemMessage, -) -from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import BaseModel - -from langchain_google_vertexai.chat_models import ChatVertexAI - -model_names_to_test = [None, "codechat-bison", "chat-bison", "gemini-pro"] - - -@pytest.mark.parametrize("model_name", model_names_to_test) -def test_initialization(model_name: Optional[str]) -> None: - """Test chat model initialization.""" - if model_name: - model = ChatVertexAI(model_name=model_name) - else: - model = ChatVertexAI() - assert model._llm_type == "vertexai" - try: - assert model.model_name == model.client._model_id - except AttributeError: - assert model.model_name == model.client._model_name.split("/")[-1] - - -@pytest.mark.parametrize("model_name", model_names_to_test) -def test_vertexai_single_call(model_name: Optional[str]) -> None: - if model_name: - model = ChatVertexAI(model_name=model_name) - else: - model = ChatVertexAI() - message = HumanMessage(content="Hello") - response = model([message]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -# mark xfail because Vertex API randomly doesn't respect -# the n/candidate_count parameter -@pytest.mark.xfail -def test_candidates() -> None: - model = ChatVertexAI(model_name="chat-bison@001", temperature=0.3, n=2) - message = HumanMessage(content="Hello") - response = model.generate(messages=[[message]]) - assert isinstance(response, LLMResult) - assert len(response.generations) == 1 - assert len(response.generations[0]) == 2 - - -@pytest.mark.parametrize("model_name", ["chat-bison@001", "gemini-pro"]) -async def test_vertexai_agenerate(model_name: str) -> None: - model = ChatVertexAI(temperature=0, model_name=model_name) - message = HumanMessage(content="Hello") - response = await model.agenerate([[message]]) - assert isinstance(response, LLMResult) - assert isinstance(response.generations[0][0].message, AIMessage) # type: ignore - - sync_response = model.generate([[message]]) - sync_generation = cast(ChatGeneration, sync_response.generations[0][0]) - async_generation = cast(ChatGeneration, response.generations[0][0]) - - # assert some properties to make debugging easier - - # xfail: this is not equivalent with temp=0 right now - # assert sync_generation.message.content == async_generation.message.content - assert sync_generation.generation_info == async_generation.generation_info - - # xfail: content is not same right now - # assert sync_generation == async_generation - - -@pytest.mark.parametrize("model_name", ["chat-bison@001", "gemini-pro"]) -def test_vertexai_stream(model_name: str) -> None: - model = ChatVertexAI(temperature=0, model_name=model_name) - message = HumanMessage(content="Hello") - - sync_response = model.stream([message]) - for chunk in sync_response: - assert isinstance(chunk, AIMessageChunk) - - -def test_vertexai_single_call_with_context() -> None: - model = ChatVertexAI() - raw_context = ( - "My name is Ned. You are my personal assistant. My favorite movies " - "are Lord of the Rings and Hobbit." - ) - question = ( - "Hello, could you recommend a good movie for me to watch this evening, please?" - ) - context = SystemMessage(content=raw_context) - message = HumanMessage(content=question) - response = model([context, message]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -def test_multimodal() -> None: - llm = ChatVertexAI(model_name="gemini-pro-vision") - gcs_url = ( - "gs://cloud-samples-data/generative-ai/image/" - "320px-Felis_catus-cat_on_snow.jpg" - ) - image_message = { - "type": "image_url", - "image_url": {"url": gcs_url}, - } - text_message = { - "type": "text", - "text": "What is shown in this image?", - } - message = HumanMessage(content=[text_message, image_message]) - output = llm([message]) - assert isinstance(output.content, str) - - -@pytest.mark.xfail(reason="problem on vertex side") -def test_multimodal_history() -> None: - llm = ChatVertexAI(model_name="gemini-pro-vision") - gcs_url = ( - "gs://cloud-samples-data/generative-ai/image/" - "320px-Felis_catus-cat_on_snow.jpg" - ) - image_message = { - "type": "image_url", - "image_url": {"url": gcs_url}, - } - text_message = { - "type": "text", - "text": "What is shown in this image?", - } - message1 = HumanMessage(content=[text_message, image_message]) - message2 = AIMessage( - content=( - "This is a picture of a cat in the snow. The cat is a tabby cat, which is " - "a type of cat with a striped coat. The cat is standing in the snow, and " - "its fur is covered in snow." - ) - ) - message3 = HumanMessage(content="What time of day is it?") - response = llm([message1, message2, message3]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -def test_vertexai_single_call_with_examples() -> None: - model = ChatVertexAI() - raw_context = "My name is Ned. You are my personal assistant." - question = "2+2" - text_question, text_answer = "4+4", "8" - inp = HumanMessage(content=text_question) - output = AIMessage(content=text_answer) - context = SystemMessage(content=raw_context) - message = HumanMessage(content=question) - response = model([context, message], examples=[inp, output]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -@pytest.mark.parametrize("model_name", model_names_to_test) -def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None: - if model_name: - model = ChatVertexAI(model_name=model_name) - else: - model = ChatVertexAI() - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - response = model([message1, message2, message3]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -def test_vertexai_single_call_fails_no_message() -> None: - chat = ChatVertexAI() - with pytest.raises(ValueError) as exc_info: - _ = chat([]) - assert ( - str(exc_info.value) - == "You should provide at least one message to start the chat!" - ) - - -@pytest.mark.parametrize("model_name", ["gemini-pro"]) -def test_chat_vertexai_gemini_system_message_error(model_name: str) -> None: - model = ChatVertexAI(model_name=model_name) - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content="You're supposed to answer math questions.") - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - with pytest.raises(ValueError): - model([system_message, message1, message2, message3]) - - -@pytest.mark.parametrize("model_name", model_names_to_test) -def test_chat_vertexai_system_message(model_name: Optional[str]) -> None: - if model_name: - model = ChatVertexAI( - model_name=model_name, convert_system_message_to_human=True - ) - else: - model = ChatVertexAI() - - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content="You're supposed to answer math questions.") - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - response = model([system_message, message1, message2, message3]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - - -@pytest.mark.parametrize("model_name", model_names_to_test) -def test_get_num_tokens_from_messages(model_name: str) -> None: - if model_name: - model = ChatVertexAI(model_name=model_name, temperature=0.0) - else: - model = ChatVertexAI(temperature=0.0) - message = HumanMessage(content="Hello") - token = model.get_num_tokens_from_messages(messages=[message]) - assert isinstance(token, int) - assert token == 3 - - -def test_chat_vertexai_gemini_function_calling() -> None: - class MyModel(BaseModel): - name: str - age: int - - model = ChatVertexAI(model_name="gemini-pro").bind(functions=[MyModel]) - message = HumanMessage(content="My name is Erick and I am 27 years old") - response = model.invoke([message]) - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - assert response.content == "" - function_call = response.additional_kwargs.get("function_call") - assert function_call - assert function_call["name"] == "MyModel" - arguments_str = function_call.get("arguments") - assert arguments_str - arguments = json.loads(arguments_str) - assert arguments == { - "name": "Erick", - "age": 27.0, - } diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_compile.py b/libs/partners/google-vertexai/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0fbd..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest - - -@pytest.mark.compile -def test_placeholder() -> None: - """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py b/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py deleted file mode 100644 index 42495629b2076..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Test Vertex AI API wrapper. - -Your end-user credentials would be used to make the calls (make sure you've run -`gcloud auth login` first). -""" -import pytest - -from langchain_google_vertexai.embeddings import VertexAIEmbeddings - - -def test_initialization() -> None: - """Test embedding model initialization.""" - VertexAIEmbeddings() - - -def test_langchain_google_vertexai_embedding_documents() -> None: - documents = ["foo bar"] - model = VertexAIEmbeddings() - output = model.embed_documents(documents) - assert len(output) == 1 - assert len(output[0]) == 768 - assert model.model_name == model.client._model_id - assert model.model_name == "textembedding-gecko@001" - - -def test_langchain_google_vertexai_embedding_query() -> None: - document = "foo bar" - model = VertexAIEmbeddings() - output = model.embed_query(document) - assert len(output) == 768 - - -def test_langchain_google_vertexai_large_batches() -> None: - documents = ["foo bar" for _ in range(0, 251)] - model_uscentral1 = VertexAIEmbeddings(location="us-central1") - model_asianortheast1 = VertexAIEmbeddings(location="asia-northeast1") - model_uscentral1.embed_documents(documents) - model_asianortheast1.embed_documents(documents) - assert model_uscentral1.instance["batch_size"] >= 250 - assert model_asianortheast1.instance["batch_size"] < 50 - - -def test_langchain_google_vertexai_paginated_texts() -> None: - documents = [ - "foo bar", - "foo baz", - "bar foo", - "baz foo", - "bar bar", - "foo foo", - "baz baz", - "baz bar", - ] - model = VertexAIEmbeddings() - output = model.embed_documents(documents) - assert len(output) == 8 - assert len(output[0]) == 768 - assert model.model_name == model.client._model_id - - -def test_warning(caplog: pytest.LogCaptureFixture) -> None: - _ = VertexAIEmbeddings() - assert len(caplog.records) == 1 - record = caplog.records[0] - assert record.levelname == "WARNING" - expected_message = ( - "Model_name will become a required arg for VertexAIEmbeddings starting from " - "Feb-01-2024. Currently the default is set to textembedding-gecko@001" - ) - assert record.message == expected_message diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_llms.py b/libs/partners/google-vertexai/tests/integration_tests/test_llms.py deleted file mode 100644 index ae10d9377cbf2..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_llms.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Test Vertex AI API wrapper. - -Your end-user credentials would be used to make the calls (make sure you've run -`gcloud auth login` first). -""" -import os -from typing import Optional - -import pytest -from langchain_core.outputs import LLMResult - -from langchain_google_vertexai.llms import VertexAI, VertexAIModelGarden - -model_names_to_test = ["text-bison@001", "gemini-pro"] -model_names_to_test_with_default = [None] + model_names_to_test - - -@pytest.mark.parametrize( - "model_name", - model_names_to_test_with_default, -) -def test_vertex_initialization(model_name: str) -> None: - llm = VertexAI(model_name=model_name) if model_name else VertexAI() - assert llm._llm_type == "vertexai" - try: - assert llm.model_name == llm.client._model_id - except AttributeError: - assert llm.model_name == llm.client._model_name.split("/")[-1] - - -@pytest.mark.parametrize( - "model_name", - model_names_to_test_with_default, -) -def test_vertex_invoke(model_name: str) -> None: - llm = ( - VertexAI(model_name=model_name, temperature=0) - if model_name - else VertexAI(temperature=0.0) - ) - output = llm.invoke("Say foo:") - assert isinstance(output, str) - - -@pytest.mark.parametrize( - "model_name", - model_names_to_test_with_default, -) -def test_vertex_generate(model_name: str) -> None: - llm = ( - VertexAI(model_name=model_name, temperature=0) - if model_name - else VertexAI(temperature=0.0) - ) - output = llm.generate(["Say foo:"]) - assert isinstance(output, LLMResult) - assert len(output.generations) == 1 - - -@pytest.mark.xfail(reason="VertexAI doesn't always respect number of candidates") -def test_vertex_generate_multiple_candidates() -> None: - llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001") - output = llm.generate(["Say foo:"]) - assert isinstance(output, LLMResult) - assert len(output.generations) == 1 - 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:"]) - assert isinstance(output, LLMResult) - assert len(output.generations) == 1 - assert len(output.generations[0]) == 2 - - -async def test_vertex_agenerate() -> None: - llm = VertexAI(temperature=0) - output = await llm.agenerate(["Please say foo:"]) - assert isinstance(output, LLMResult) - - -@pytest.mark.parametrize( - "model_name", - model_names_to_test_with_default, -) -def test_stream(model_name: str) -> None: - llm = ( - VertexAI(temperature=0, model_name=model_name) - if model_name - else VertexAI(temperature=0) - ) - for token in llm.stream("I'm Pickle Rick"): - assert isinstance(token, str) - - -async def test_vertex_consistency() -> None: - llm = VertexAI(temperature=0) - output = llm.generate(["Please say foo:"]) - streaming_output = llm.generate(["Please say foo:"], stream=True) - async_output = await llm.agenerate(["Please say foo:"]) - assert output.generations[0][0].text == streaming_output.generations[0][0].text - 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)], -) -def test_model_garden( - endpoint_os_variable_name: str, result_arg: Optional[str] -) -> None: - """In order to run this test, you should provide endpoint names. - - Example: - export FALCON_ENDPOINT_ID=... - export LLAMA_ENDPOINT_ID=... - export PROJECT=... - """ - endpoint_id = os.environ[endpoint_os_variable_name] - project = os.environ["PROJECT"] - location = "europe-west4" - llm = VertexAIModelGarden( - endpoint_id=endpoint_id, - project=project, - result_arg=result_arg, - location=location, - ) - output = llm("What is the meaning of life?") - assert isinstance(output, str) - 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)], -) -def test_model_garden_generate( - endpoint_os_variable_name: str, result_arg: Optional[str] -) -> None: - """In order to run this test, you should provide endpoint names. - - Example: - export FALCON_ENDPOINT_ID=... - export LLAMA_ENDPOINT_ID=... - export PROJECT=... - """ - endpoint_id = os.environ[endpoint_os_variable_name] - project = os.environ["PROJECT"] - location = "europe-west4" - llm = VertexAIModelGarden( - endpoint_id=endpoint_id, - project=project, - result_arg=result_arg, - location=location, - ) - output = llm.generate(["What is the meaning of life?", "How much is 2+2"]) - assert isinstance(output, LLMResult) - 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", - [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)], -) -async def test_model_garden_agenerate( - endpoint_os_variable_name: str, result_arg: Optional[str] -) -> None: - endpoint_id = os.environ[endpoint_os_variable_name] - project = os.environ["PROJECT"] - location = "europe-west4" - llm = VertexAIModelGarden( - endpoint_id=endpoint_id, - project=project, - result_arg=result_arg, - location=location, - ) - output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"]) - assert isinstance(output, LLMResult) - assert len(output.generations) == 2 - - -@pytest.mark.parametrize( - "model_name", - model_names_to_test, -) -def test_vertex_call_count_tokens(model_name: str) -> None: - llm = VertexAI(model_name=model_name) - output = llm.get_num_tokens("How are you?") - assert output == 4 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 deleted file mode 100644 index 7e526cbf27ad5..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_llms_safety.py +++ /dev/null @@ -1,97 +0,0 @@ -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/integration_tests/test_tools.py b/libs/partners/google-vertexai/tests/integration_tests/test_tools.py deleted file mode 100644 index 5da7224005d47..0000000000000 --- a/libs/partners/google-vertexai/tests/integration_tests/test_tools.py +++ /dev/null @@ -1,172 +0,0 @@ -import os -import re -from typing import Any, List, Union - -from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish -from langchain_core.messages import AIMessageChunk -from langchain_core.output_parsers import BaseOutputParser -from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.tools import Tool - -from langchain_google_vertexai.chat_models import ChatVertexAI - - -class _TestOutputParser(BaseOutputParser): - def parse_result( - self, result: List[Generation], *, partial: bool = False - ) -> Union[AgentAction, AgentFinish]: - if not isinstance(result[0], ChatGeneration): - raise ValueError("This output parser only works on ChatGeneration output") - message = result[0].message - function_call = message.additional_kwargs.get("function_call", {}) - if function_call: - function_name = function_call["name"] - tool_input = function_call.get("arguments", {}) - - content_msg = f"responded: {message.content}\n" if message.content else "\n" - log_msg = ( - f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n" - ) - return AgentActionMessageLog( - tool=function_name, - tool_input=tool_input, - log=log_msg, - message_log=[message], - ) - - return AgentFinish( - return_values={"output": message.content}, log=str(message.content) - ) - - def parse(self, text: str) -> Union[AgentAction, AgentFinish]: - raise ValueError("Can only parse messages") - - -def test_tools() -> None: - from langchain.agents import AgentExecutor - from langchain.agents.format_scratchpad import ( - format_to_openai_function_messages, - ) - from langchain.chains import LLMMathChain - - llm = ChatVertexAI(model_name="gemini-pro") - math_chain = LLMMathChain.from_llm(llm=llm) - tools = [ - Tool( - name="Calculator", - func=math_chain.run, - description="useful for when you need to answer questions about math", - ) - ] - prompt = ChatPromptTemplate.from_messages( - [ - ("user", "{input}"), - MessagesPlaceholder(variable_name="agent_scratchpad"), - ] - ) - llm_with_tools = llm.bind(functions=tools) - - agent: Any = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_to_openai_function_messages( - x["intermediate_steps"] - ), - } - | prompt - | llm_with_tools - | _TestOutputParser() - ) - agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) - - response = agent_executor.invoke({"input": "What is 6 raised to the 0.43 power?"}) - assert isinstance(response, dict) - assert response["input"] == "What is 6 raised to the 0.43 power?" - - # convert string " The result is 2.160752567226312" to just numbers/periods - # use regex to find \d+\.\d+ - just_numbers = re.findall(r"\d+\.\d+", response["output"])[0] - - assert round(float(just_numbers), 2) == 2.16 - - -def test_stream() -> None: - from langchain.chains import LLMMathChain - - llm = ChatVertexAI(model_name="gemini-pro") - math_chain = LLMMathChain.from_llm(llm=llm) - tools = [ - Tool( - name="Calculator", - func=math_chain.run, - description="useful for when you need to answer questions about math", - ) - ] - response = list(llm.stream("What is 6 raised to the 0.43 power?", functions=tools)) - assert len(response) == 1 - assert isinstance(response[0], AIMessageChunk) - assert "function_call" in response[0].additional_kwargs - - -def test_multiple_tools() -> None: - from langchain.agents import AgentExecutor - from langchain.agents.format_scratchpad import format_to_openai_function_messages - from langchain.chains import LLMMathChain - from langchain.utilities import ( - GoogleSearchAPIWrapper, - ) - - llm = ChatVertexAI(model_name="gemini-pro", max_output_tokens=1024) - math_chain = LLMMathChain.from_llm(llm=llm) - google_search_api_key = os.environ["GOOGLE_SEARCH_API_KEY"] - google_cse_id = os.environ["GOOGLE_CSE_ID"] - search = GoogleSearchAPIWrapper( - k=10, google_api_key=google_search_api_key, google_cse_id=google_cse_id - ) - tools = [ - Tool( - name="Calculator", - func=math_chain.run, - description="useful for when you need to answer questions about math", - ), - Tool( - name="Search", - func=search.run, - description=( - "useful for when you need to answer questions about current events. " - "You should ask targeted questions" - ), - ), - ] - prompt = ChatPromptTemplate.from_messages( - [ - ("user", "{input}"), - MessagesPlaceholder(variable_name="agent_scratchpad"), - ] - ) - llm_with_tools = llm.bind(functions=tools) - - agent: Any = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_to_openai_function_messages( - x["intermediate_steps"] - ), - } - | prompt - | llm_with_tools - | _TestOutputParser() - ) - agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) - - question = ( - "Who is Leo DiCaprio's girlfriend? What is her " - "current age raised to the 0.43 power?" - ) - response = agent_executor.invoke({"input": question}) - assert isinstance(response, dict) - assert response["input"] == question - - # xfail: not getting age in search result most of time - # assert "3.850" in response["output"] diff --git a/libs/partners/google-vertexai/tests/unit_tests/__init__.py b/libs/partners/google-vertexai/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py deleted file mode 100644 index f24f418ba6253..0000000000000 --- a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py +++ /dev/null @@ -1,318 +0,0 @@ -"""Test chat model integration.""" - -import json -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional -from unittest.mock import MagicMock, Mock, patch - -import pytest -from google.cloud.aiplatform_v1beta1.types import ( - Content, - FunctionCall, - Part, -) -from google.cloud.aiplatform_v1beta1.types import ( - content as gapic_content_types, -) -from langchain_core.messages import ( - AIMessage, - HumanMessage, - SystemMessage, -) -from vertexai.language_models import ChatMessage, InputOutputTextPair # type: ignore -from vertexai.preview.generative_models import ( # type: ignore - Candidate, -) - -from langchain_google_vertexai.chat_models import ( - ChatVertexAI, - _parse_chat_history, - _parse_chat_history_gemini, - _parse_examples, - _parse_response_candidate, -) - - -def test_parse_examples_correct() -> None: - text_question = ( - "Hello, could you recommend a good movie for me to watch this evening, please?" - ) - question = HumanMessage(content=text_question) - text_answer = ( - "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring " - "(2001): This is the first movie in the Lord of the Rings trilogy." - ) - answer = AIMessage(content=text_answer) - examples = _parse_examples([question, answer, question, answer]) - assert len(examples) == 2 - assert examples == [ - InputOutputTextPair(input_text=text_question, output_text=text_answer), - InputOutputTextPair(input_text=text_question, output_text=text_answer), - ] - - -def test_parse_examples_failes_wrong_sequence() -> None: - with pytest.raises(ValueError) as exc_info: - _ = _parse_examples([AIMessage(content="a")]) - assert ( - str(exc_info.value) - == "Expect examples to have an even amount of messages, got 1." - ) - - -@dataclass -class StubTextChatResponse: - """Stub text-chat response from VertexAI for testing.""" - - text: str - - -@pytest.mark.parametrize("stop", [None, "stop1"]) -def test_vertexai_args_passed(stop: Optional[str]) -> None: - response_text = "Goodbye" - user_prompt = "Hello" - prompt_params: Dict[str, Any] = { - "max_output_tokens": 1, - "temperature": 10000.0, - "top_k": 10, - "top_p": 0.5, - } - - # Mock the library to ensure the args are passed correctly - with patch("vertexai._model_garden._model_garden_models._from_pretrained") as mg: - mock_response = MagicMock() - mock_response.candidates = [StubTextChatResponse(text=response_text)] - mock_chat = MagicMock() - mock_send_message = MagicMock(return_value=mock_response) - mock_chat.send_message = mock_send_message - - mock_model = MagicMock() - mock_start_chat = MagicMock(return_value=mock_chat) - mock_model.start_chat = mock_start_chat - mg.return_value = mock_model - - model = ChatVertexAI(**prompt_params) - message = HumanMessage(content=user_prompt) - if stop: - response = model([message], stop=[stop]) - else: - response = model([message]) - - assert response.content == response_text - mock_send_message.assert_called_once_with(user_prompt, candidate_count=1) - expected_stop_sequence = [stop] if stop else None - mock_start_chat.assert_called_once_with( - context=None, - message_history=[], - **prompt_params, - stop_sequences=expected_stop_sequence, - ) - - -def test_parse_chat_history_correct() -> None: - text_context = ( - "My name is Ned. You are my personal assistant. My " - "favorite movies are Lord of the Rings and Hobbit." - ) - context = SystemMessage(content=text_context) - text_question = ( - "Hello, could you recommend a good movie for me to watch this evening, please?" - ) - question = HumanMessage(content=text_question) - text_answer = ( - "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring " - "(2001): This is the first movie in the Lord of the Rings trilogy." - ) - answer = AIMessage(content=text_answer) - history = _parse_chat_history([context, question, answer, question, answer]) - assert history.context == context.content - assert len(history.history) == 4 - assert history.history == [ - ChatMessage(content=text_question, author="user"), - ChatMessage(content=text_answer, author="bot"), - ChatMessage(content=text_question, author="user"), - ChatMessage(content=text_answer, author="bot"), - ] - - -def test_parse_history_gemini() -> None: - system_input = "You're supposed to answer math questions." - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - system_message = SystemMessage(content=system_input) - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - messages = [system_message, message1, message2, message3] - history = _parse_chat_history_gemini(messages, convert_system_message_to_human=True) - assert len(history) == 3 - assert history[0].role == "user" - assert history[0].parts[0].text == system_input - assert history[0].parts[1].text == text_question1 - assert history[1].role == "model" - assert history[1].parts[0].text == text_answer1 - - -def test_default_params_palm() -> None: - user_prompt = "Hello" - - with patch("vertexai._model_garden._model_garden_models._from_pretrained") as mg: - mock_response = MagicMock() - mock_response.candidates = [StubTextChatResponse(text="Goodbye")] - mock_chat = MagicMock() - mock_send_message = MagicMock(return_value=mock_response) - mock_chat.send_message = mock_send_message - - mock_model = MagicMock() - mock_start_chat = MagicMock(return_value=mock_chat) - mock_model.start_chat = mock_start_chat - mg.return_value = mock_model - - model = ChatVertexAI(model_name="text-bison@001") - message = HumanMessage(content=user_prompt) - _ = model([message]) - mock_start_chat.assert_called_once_with( - context=None, - message_history=[], - max_output_tokens=128, - top_k=40, - top_p=0.95, - stop_sequences=None, - ) - - -@dataclass -class StubGeminiResponse: - """Stub gemini response from VertexAI for testing.""" - - text: str - content: Any - citation_metadata: Any - safety_ratings: List[Any] = field(default_factory=list) - - -def test_default_params_gemini() -> None: - user_prompt = "Hello" - - with patch("langchain_google_vertexai.chat_models.GenerativeModel") as gm: - mock_response = MagicMock() - mock_response.candidates = [ - StubGeminiResponse( - text="Goodbye", - content=Mock(parts=[Mock(function_call=None)]), - citation_metadata=None, - ) - ] - mock_chat = MagicMock() - mock_send_message = MagicMock(return_value=mock_response) - mock_chat.send_message = mock_send_message - - mock_model = MagicMock() - mock_start_chat = MagicMock(return_value=mock_chat) - mock_model.start_chat = mock_start_chat - gm.return_value = mock_model - model = ChatVertexAI(model_name="gemini-pro") - message = HumanMessage(content=user_prompt) - _ = model([message]) - mock_start_chat.assert_called_once_with(history=[]) - - -@pytest.mark.parametrize( - "raw_candidate, expected", - [ - ( - gapic_content_types.Candidate( - content=Content( - role="model", - parts=[ - Part( - function_call=FunctionCall( - name="Information", - args={"name": "Ben"}, - ), - ) - ], - ) - ), - { - "name": "Information", - "arguments": {"name": "Ben"}, - }, - ), - ( - gapic_content_types.Candidate( - content=Content( - role="model", - parts=[ - Part( - function_call=FunctionCall( - name="Information", - args={"info": ["A", "B", "C"]}, - ), - ) - ], - ) - ), - { - "name": "Information", - "arguments": {"info": ["A", "B", "C"]}, - }, - ), - ( - gapic_content_types.Candidate( - content=Content( - role="model", - parts=[ - Part( - function_call=FunctionCall( - name="Information", - args={ - "people": [ - {"name": "Joe", "age": 30}, - {"name": "Martha"}, - ] - }, - ), - ) - ], - ) - ), - { - "name": "Information", - "arguments": { - "people": [ - {"name": "Joe", "age": 30}, - {"name": "Martha"}, - ] - }, - }, - ), - ( - gapic_content_types.Candidate( - content=Content( - role="model", - parts=[ - Part( - function_call=FunctionCall( - name="Information", - args={"info": [[1, 2, 3], [4, 5, 6]]}, - ), - ) - ], - ) - ), - { - "name": "Information", - "arguments": {"info": [[1, 2, 3], [4, 5, 6]]}, - }, - ), - ], -) -def test_parse_response_candidate(raw_candidate, expected) -> None: - response_candidate = Candidate._from_gapic(raw_candidate) - result = _parse_response_candidate(response_candidate) - result_arguments = json.loads( - result.additional_kwargs["function_call"]["arguments"] - ) - - assert result_arguments == expected["arguments"] diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_imports.py b/libs/partners/google-vertexai/tests/unit_tests/test_imports.py deleted file mode 100644 index 7afa74f1dc7ce..0000000000000 --- a/libs/partners/google-vertexai/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,16 +0,0 @@ -from langchain_google_vertexai import __all__ - -EXPECTED_ALL = [ - "ChatVertexAI", - "VertexAIEmbeddings", - "VertexAI", - "VertexAIModelGarden", - "HarmBlockThreshold", - "HarmCategory", - "PydanticFunctionsOutputParser", - "create_structured_runnable", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__)