_reasoning_engines.py

# -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import abc
import inspect
import io
import os
import sys
import tarfile
import typing
from typing import Optional, Protocol, Sequence, Union

from google.cloud.aiplatform import base
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform import utils as aip_utils
from google.cloud.aiplatform_v1beta1 import types
from vertexai.reasoning_engines import _utils


_LOGGER = base.Logger(__name__)
_SUPPORTED_PYTHON_VERSIONS = ("3.8", "3.9", "3.10", "3.11")
_DEFAULT_GCS_DIR_NAME = "reasoning_engine"
_BLOB_FILENAME = "reasoning_engine.pkl"
_REQUIREMENTS_FILE = "requirements.txt"
_EXTRA_PACKAGES_FILE = "dependencies.tar.gz"


@typing.runtime_checkable
class Queryable(Protocol):
    """Protocol for Reasoning Engine applications that can be queried."""

    @abc.abstractmethod
    def query(self, **kwargs):
        """Runs the Reasoning Engine to serve the user query."""


class ReasoningEngine(base.VertexAiResourceNounWithFutureManager, Queryable):
    """Represents a Vertex AI Reasoning Engine resource."""
    client_class = aip_utils.ReasoningEngineClientWithOverride
    _resource_noun = "reasoning_engine"
    _getter_method = "get_reasoning_engine"
    _list_method = "list_reasoning_engines"
    _delete_method = "delete_reasoning_engine"
    _parse_resource_name_method = "parse_reasoning_engine_path"
    _format_resource_name_method = "reasoning_engine_path"

    def __init__(self, reasoning_engine_name: str):
        """Retrieves a Reasoning Engine resource.

        Args:
            reasoning_engine_name (str):
                Required. A fully-qualified resource name or ID such as
                "projects/123/locations/us-central1/reasoningEngines/456" or
                "456" when project and location are initialized or passed.
        """
        super().__init__(resource_name=reasoning_engine_name)
        self.execution_api_client = initializer.global_config.create_client(
            client_class=aip_utils.ReasoningEngineExecutionClientWithOverride,
        )
        self._gca_resource = self._get_gca_resource(
            resource_name=reasoning_engine_name
        )
        self._operation_schemas = None

    @property
    def resource_name(self) -> str:
        """Fully-qualified resource name."""
        return self._gca_resource.name

    @classmethod
    def create(
            cls,
            reasoning_engine: Queryable,
            *,
            requirements: Optional[Union[str, Sequence[str]]] = None,
            reasoning_engine_name: Optional[str] = None,
            display_name: Optional[str] = None,
            description: Optional[str] = None,
            gcs_dir_name: str = _DEFAULT_GCS_DIR_NAME,
            sys_version: Optional[str] = None,
            extra_packages: Optional[Sequence[str]] = None,
        ) -> "ReasoningEngine":
        """Creates a new ReasoningEngine.

        The Reasoning Engine will be an instance of the `reasoning_engine` that
        was passed in, running remotely on Vertex AI.

        Sample ``src_dir`` contents (e.g. ``./user_src_dir``):

        .. code-block:: python

            user_src_dir/
            |-- main.py
            |-- requirements.txt
            |-- user_code/
            |   |-- utils.py
            |   |-- ...
            |-- ...

        To build a Reasoning Engine:

        .. code-block:: python

            remote_app = ReasoningEngine.create(
                local_app,
                requirements=[
                    # I.e. the PyPI dependencies listed in requirements.txt
                    "google-cloud-aiplatform==1.25.0",
                    "langchain==0.0.242",
                    ...
                ],
                extra_packages=[
                    "./user_src_dir/main.py", # a single file
                    "./user_src_dir/user_code", # a directory
                    ...
                ],
            )

        Args:
            reasoning_engine (ReasoningEngineInterface):
                Required. The Reasoning Engine to be created.
            requirements (Union[str, Sequence[str]]):
                Optional. The set of PyPI dependencies needed. It can either be
                the path to a single file (requirements.txt), or an ordered list
                of strings corresponding to each line of the requirements file.
            reasoning_engine_name (str):
                Optional. A fully-qualified resource name or ID such as
                "projects/123/locations/us-central1/reasoningEngines/456" or
                "456" when project and location are initialized or passed. If
                specifying the ID, it should be 4-63 characters. Valid
                characters are lowercase letters, numbers and hyphens ("-"),
                and it should start with a number or a lower-case letter. If not
                provided, Vertex AI will generate a value for this ID.
            display_name (str):
                Optional. The user-defined name of the Reasoning Engine.
                The name can be up to 128 characters long and can comprise any
                UTF-8 character.
            description (str):
                Optional. The description of the Reasoning Engine.
            gcs_dir_name (CreateReasoningEngineOptions):
                Optional. The GCS bucket directory under `staging_bucket` to
                use for staging the artifacts needed.
            sys_version (str):
                Optional. The Python system version used. Currently supports any
                of "3.8", "3.9", "3.10", "3.11". If not specified, it defaults
                to the "{major}.{minor}" attributes of sys.version_info.
            extra_packages (Sequence[str]):
                Optional. The set of extra user-provided packages (if any).

        Returns:
            ReasoningEngine: The Reasoning Engine that was created.

        Raises:
            ValueError: If `sys.version` is not supported by ReasoningEngine.
            ValueError: If the `project` was not set using `vertexai.init`.
            ValueError: If the `location` was not set using `vertexai.init`.
            ValueError: If the `staging_bucket` was not set using vertexai.init.
            ValueError: If the `staging_bucket` does not start with "gs://".
            FileNotFoundError: If `extra_packages` includes a file or directory
            that does not exist.
        """
        if not sys_version:
            sys_version = f"{sys.version_info.major}.{sys.version_info.minor}"
        if sys_version not in _SUPPORTED_PYTHON_VERSIONS:
            raise ValueError(
                f"Unsupported python version: {sys_version}. ReasoningEngine "
                f"only supports {_SUPPORTED_PYTHON_VERSIONS} at the moment."
            )
        sdk_resource = cls.__new__(cls)
        base.VertexAiResourceNounWithFutureManager.__init__(
            sdk_resource,
            resource_name=reasoning_engine_name,
        )
        staging_bucket = initializer.global_config.staging_bucket
        if not staging_bucket:
            raise ValueError(
                "Please provide a `staging_bucket` in `vertexai.init(...)`"
            )
        if not staging_bucket.startswith("gs://"):
            raise ValueError(f"{staging_bucket=} must start with `gs://`")
        if not (
                hasattr(reasoning_engine, "query")
                and callable(reasoning_engine.query)
            ):
            raise TypeError(
                "reasoning_engine does not have a callable method named `query`"
            )
        try:
            inspect.signature(reasoning_engine.query)
        except ValueError as err:
            raise ValueError(
                "Invalid query signature. This might be due to a missing "
                "`self` argument in the reasoning_engine.query method."
            ) from err
        if isinstance(requirements, str):
            try:
                _LOGGER.info(f"Reading requirements from {requirements=}")
                with open(requirements) as f:
                    requirements = f.read().splitlines()
                    _LOGGER.info(f"Read the following lines: {requirements}")
            except IOError as err:
                raise IOError(
                    f"Failed to read requirements from {requirements=}"
                ) from err
        requirements = requirements or []
        extra_packages = extra_packages or []
        for extra_package in extra_packages:
            if not os.path.exists(extra_package):
                raise FileNotFoundError(
                    f"Extra package specified but not found: {extra_package=}"
                )
        # Prepares the Reasoning Engine for creation in Vertex AI.
        # This involves packaging and uploading the artifacts for
        # reasoning_engine, requirements and extra_packages to
        # `staging_bucket/gcs_dir_name`.
        _prepare(
            reasoning_engine=reasoning_engine,
            requirements=requirements,
            project=sdk_resource.project,
            location=sdk_resource.location,
            staging_bucket=staging_bucket,
            gcs_dir_name=gcs_dir_name,
            extra_packages=extra_packages,
        )
        package_spec = types.ReasoningEngineSpec.PackageSpec(
            python_version=sys_version,
            pickle_object_gcs_uri="{}/{}/{}".format(
                staging_bucket,
                gcs_dir_name,
                _BLOB_FILENAME,
            ),
            dependency_files_gcs_uri="{}/{}/{}".format(
                staging_bucket,
                gcs_dir_name,
                _EXTRA_PACKAGES_FILE,
            ),
        )
        if requirements:
            package_spec.requirements_gcs_uri = "{}/{}/{}".format(
                staging_bucket,
                gcs_dir_name,
                _REQUIREMENTS_FILE,
            )
        reasoning_engine_spec = types.ReasoningEngineSpec(
            package_spec=package_spec,
        )
        try:
            schema_dict = _utils.generate_schema(
                reasoning_engine.query,
                schema_name=f"{type(reasoning_engine).__name__}_query",
            )
            # Note: we append the schema post-initialization to avoid upstream
            # issues in marshaling the data that would result in errors like:
            # ../../../../../proto/marshal/rules/struct.py:140: in to_proto
            #  self._marshal.to_proto(struct_pb2.Value, v) for k, v in value.items()
            # E   AttributeError: 'list' object has no attribute 'items'
            reasoning_engine_spec.class_methods.append(_utils.to_proto(schema_dict))
        except Exception as e:
            _LOGGER.warning(f"failed to generate schema: {e}")
        operation_future = sdk_resource.api_client.create_reasoning_engine(
            parent=initializer.global_config.common_location_path(
                project=sdk_resource.project, location=sdk_resource.location
            ),
            reasoning_engine=types.ReasoningEngine(
                name=reasoning_engine_name,
                display_name=display_name,
                description=description,
                spec=reasoning_engine_spec,
            ),
        )
        _LOGGER.log_create_with_lro(cls, operation_future)
        created_resource = operation_future.result()
        _LOGGER.log_create_complete(
            cls,
            created_resource,
            cls._resource_noun,
            module_name="vertexai.preview.reasoning_engines",
        )
        # We use `._get_gca_resource(...)` instead of `created_resource` to
        # fully instantiate the attributes of the reasoning engine.
        sdk_resource._gca_resource = sdk_resource._get_gca_resource(
            resource_name=created_resource.name
        )
        sdk_resource.execution_api_client = (
            initializer.global_config.create_client(
                client_class=aip_utils.ReasoningEngineExecutionClientWithOverride,
                credentials=sdk_resource.credentials,
                location_override=sdk_resource.location,
            )
        )
        sdk_resource._operation_schemas = None
        return sdk_resource

    def operation_schemas(self) -> Sequence[_utils.JsonDict]:
        """Returns the (Open)API schemas for the Reasoning Engine."""
        spec = _utils.to_dict(self._gca_resource.spec)
        if self._operation_schemas is None:
            self._operation_schemas = spec.get("classMethods", [])
        return self._operation_schemas

    def query(self, **kwargs) -> _utils.JsonDict:
        """Runs the Reasoning Engine to serve the user query.

        This will be based on the `.query(...)` method of the python object that
        was passed in when creating the Reasoning Engine.

        Args:
            **kwargs:
                Optional. The arguments of the `.query(...)` method.

        Returns:
            dict[str, Any]: The response from serving the user query.
        """
        response = self.execution_api_client.query_reasoning_engine(
            request=types.QueryReasoningEngineRequest(
                name=self.resource_name,
                input=kwargs,
            ),
        )
        output = _utils.to_dict(response)
        if "output" in output:
            return output.get("output")
        return output


def _prepare(
        reasoning_engine: Queryable,
        requirements: Sequence[str],
        project: str,
        location: str,
        staging_bucket: str,
        gcs_dir_name: str,
        extra_packages: Sequence[str],
    ) -> None:
    """Prepares the reasoning engine for creation in Vertex AI.

    This involves packaging and uploading the artifacts to Cloud Storage.

    Args:
        reasoning_engine: The reasoning engine to be prepared.
        requirements (Sequence[str]): The set of PyPI dependencies needed.
        project (str): The project for the staging bucket.
        location (str): The location for the staging bucket.
        staging_bucket (str): The staging bucket name in the form "gs://...".
        gcs_dir_name (str): The GCS bucket directory under `staging_bucket` to
            use for staging the artifacts needed.
        extra_packages (Sequence[str]): The set of extra user-provided packages.
    """
    try:
        from google.cloud.exceptions import NotFound
    except:
        NotFound = Exception
    storage = _utils._import_cloud_storage_or_raise()
    cloudpickle = _utils._import_cloudpickle_or_raise()
    storage_client = storage.Client(project=project)
    staging_bucket = staging_bucket.replace("gs://", "")
    try:
        gcs_bucket = storage_client.get_bucket(staging_bucket)
        _LOGGER.info(f"Using bucket {staging_bucket}")
    except NotFound:
        new_bucket = storage_client.bucket(staging_bucket)
        gcs_bucket = storage_client.create_bucket(new_bucket, location=location)
        _LOGGER.info(f"Creating bucket {staging_bucket} in {location=}")

    blob = gcs_bucket.blob(os.path.join(gcs_dir_name, _BLOB_FILENAME))
    with blob.open("wb") as f:
        cloudpickle.dump(reasoning_engine, f)
    dir_name = f"gs://{staging_bucket}/{gcs_dir_name}"
    _LOGGER.info(f"Writing to {dir_name}/{_BLOB_FILENAME}")

    blob = gcs_bucket.blob(os.path.join(gcs_dir_name, _REQUIREMENTS_FILE))
    if requirements:
        blob.upload_from_string("\n".join(requirements))
        _LOGGER.info(f"Writing to {dir_name}/{_REQUIREMENTS_FILE}")

    _LOGGER.info("Creating in-memory tarfile of extra_packages")
    tar_fileobj = io.BytesIO()
    with tarfile.open(fileobj=tar_fileobj, mode="w|gz") as tar:
        for file in extra_packages:
            tar.add(file)
    tar_fileobj.seek(0)
    blob = gcs_bucket.blob(os.path.join(gcs_dir_name, _EXTRA_PACKAGES_FILE))
    blob.upload_from_string(tar_fileobj.read())
    _LOGGER.info(f"Writing to {dir_name}/{_EXTRA_PACKAGES_FILE}")