From 3ea3225db4cb9fee081305b3b7698722716d7494 Mon Sep 17 00:00:00 2001 From: tibo7 Date: Thu, 31 Oct 2024 11:15:48 +0000 Subject: [PATCH] feat: add agentic rag vertex ai search pattern --- .../e2e-gen-ai-app-starter-pack/app/README.md | 5 + .../e2e-gen-ai-app-starter-pack/app/chain.py | 8 +- .../agentic_rag_vertex_ai_search/README.md | 154 ++ .../pattern_setup/prepare_pattern.py | 134 ++ .../resources_to_copy/app/chain.py | 139 ++ .../resources_to_copy/app/retrievers.py | 74 + .../resources_to_copy/app/templates.py | 53 + .../data_processing/README.md | 7 + .../data_processing_pipeline/components.py | 270 ++++ .../data_processing_pipeline/pipeline.py | 38 + .../submit_pipeline.py | 188 +++ .../data_processing/poetry.lock | 1205 +++++++++++++++ .../data_processing/pyproject.toml | 15 + .../deployment/cd/deploy-to-prod.yaml | 63 + .../deployment/cd/staging.yaml | 163 ++ .../terraform/append__iam.tf_updates | 17 + .../append__service_accounts.tf_updates | 11 + .../terraform/append__storage.tf_updates | 11 + .../terraform/append__variables.tf_updates | 44 + .../terraform/data_store.tf_updates | 66 + .../terraform/dev/append__iam.tf_updates | 15 + .../dev/append__service_accounts.tf_updates | 12 + .../terraform/dev/append__storage.tf_updates | 10 + .../dev/append__variables.tf_updates | 39 + .../terraform/dev/data_store.tf_updates | 31 + .../dev/vars/append__env.tfvars_updates | 7 + ...ubstitute__cd_pipeline_triggers.tf_updates | 6 + ...eploy_to_prod_pipeline_triggers.tf_updates | 6 + .../terraform/vars/append__env.tfvars_updates | 8 + .../tests/integration/test_chain.py | 77 + .../app/patterns/custom_rag_qa/chain.py | 7 +- .../patterns/langgraph_dummy_agent/chain.py | 5 +- .../e2e-gen-ai-app-starter-pack/app/server.py | 2 + .../deployment/README.md | 7 + .../deployment/cd/deploy-to-prod.yaml | 2 +- .../deployment/cd/staging.yaml | 3 +- .../deployment/terraform/apis.tf | 34 +- .../deployment/terraform/build_triggers.tf | 12 +- .../deployment/terraform/dev/iam.tf | 1 - .../deployment/terraform/dev/variables.tf | 2 +- .../deployment/terraform/iam.tf | 19 +- .../deployment/terraform/locals.tf | 36 + .../deployment/terraform/log_sinks.tf | 10 +- .../deployment/terraform/service_accounts.tf | 2 +- .../deployment/terraform/storage.tf | 11 +- .../deployment/terraform/variables.tf | 7 +- .../e2e-gen-ai-app-starter-pack/poetry.lock | 1377 +++++++++-------- .../pyproject.toml | 24 +- .../tests/integration/test_server_e2e.py | 10 +- 49 files changed, 3691 insertions(+), 756 deletions(-) create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/README.md create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/prepare_pattern.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/chain.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/retrievers.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/templates.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/README.md create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/components.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/pipeline.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/submit_pipeline.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/poetry.lock create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/pyproject.toml create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/cd/deploy-to-prod.yaml create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/cd/staging.yaml create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__iam.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__service_accounts.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__storage.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__variables.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/data_store.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__iam.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__service_accounts.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__storage.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__variables.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/data_store.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/vars/append__env.tfvars_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__cd_pipeline_triggers.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__deploy_to_prod_pipeline_triggers.tf_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/vars/append__env.tfvars_updates create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/tests/integration/test_chain.py create mode 100644 gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/locals.tf diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/README.md index 7f96c1a27c8..fbc27ecfbb5 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/README.md +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/README.md @@ -11,6 +11,7 @@ This folder implements a chatbot application using FastAPI, and Google Cloud ser ├── patterns/ # Conversation pattern implementations │ ├── custom_rag_qa/ │ └── langgraph_dummy_agent/ +│ └── agent_builder_search/ ├── utils/ # Utility functions and classes └── eval/ # Evaluation tools and data ``` @@ -31,6 +32,10 @@ This approach offers maximum flexibility in the orchestration of steps and allow A simple agent implemented using LangGraph, a framework for building agent and multi-agent workflows. +### 4. RAG with Vertex AI Agent Builder Search + +A RAG (Retrieval-Augmented Generation) pattern using Vertex AI Agent Builder Search for the retrieval of information. It includes a data ingestion pipeline to automate the update of your data. For more details on how to set it up please review the related readme file under the respective folder. + ### Switching Between Patterns To switch between different patterns, modify the import statement in `server.py`. diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/chain.py index d367bd1ad70..9b1ba51ae0c 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/chain.py +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/chain.py @@ -15,13 +15,17 @@ from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_google_vertexai import ChatVertexAI +LOCATION = "us-central1" +LLM = "gemini-1.5-flash-002" + + llm = ChatVertexAI( - model_name="gemini-1.5-flash-002", + model_name=LLM, + location=LOCATION, temperature=0, max_output_tokens=1024, ) - template = ChatPromptTemplate.from_messages( [ ( diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/README.md new file mode 100644 index 00000000000..c8506912bad --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/README.md @@ -0,0 +1,154 @@ +# Agentic RAG with Vertex AI Search + +This pattern enhances the Gen AI App Starter Pack with a production-ready data ingestion pipeline, enriching your Retrieval Augmented Generation (RAG) applications. Using Vertex AI Search's state-of-the-art search capabilities, you can ingest, process, and embed custom data, improving the relevance and context of your generated responses. + +The pattern provides the infrastructure to create a Vertex AI Pipeline with your custom code. Because it's built on Vertex AI Pipelines, you benefit from features like scheduled runs, recurring executions, and on-demand triggers. For processing terabyte-scale data, we recommend combining Vertex AI Pipelines with data analytics tools like BigQuery or Dataflow. + +## Architecture + +The pattern implements the following architecture: + +![architecture diagram](https://storage.googleapis.com/github-repo/generative-ai/sample-apps/e2e-gen-ai-app-starter-pack/agentic_rag_vertex_ai_search_architecture.png) +The main addition to the base pattern is the addition of the ingestion components. + +### Key Features + +- **Vertex AI Search Integration:** Utilizes Vertex AI Search for efficient data storage and retrieval. +- **Automated Data Ingestion Pipeline:** Automates the process of ingesting data from input sources. +- **Custom Embeddings:** Generates embeddings using Vertex AI Embeddings and incorporates them into your data for enhanced semantic search. +- **Terraform Deployment:** Ingestion pipeline is instantiated with Terraform alongside the rest of the infrastructure of the starter pack. +- **Cloud Build Integration:** Deployment of ingestion pipelines is added to the CD pipelines of the starter pack. +- **Customizable Code:** Easily adapt and customize the code to fit your specific application needs and data sources. + +From an infrastructure point of view, a _Vertex AI Agent Builder Datastore_ and _Search App_ are being initialised in both staging and prod environments. You can learn more about these [here](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction). + +When a new build is triggered through a commit to the main branch, in addition to updating the backend application, the data ingestion pipeline is also updated. + +The data ingestion is orchestrated through a Vertex AI [Pipeline](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) which in its simplest form comprises of a two processing step. +The pipeline reads data from a source (e.g., a PDF document) located at a configurable location. The data is then divided into smaller chunks, processed, and ingested into the Vertex AI Agent Builder Datastore. Upon ingestion completion, the connected Search App is automatically updated with the new data without any downtime. + +Please note that the ingestion in the example is set to run automatically once per week. You may change the frequency of the update or the triggering mechanism altogether to match your needs. Look into the `data_processing/data_processing_pipeline/pipeline.py` and `deployment/cd/deploy-to-prod.yaml` files as the starting point for these changes. + +## Getting Started + +1. **Prepare the pattern:** First, prepare the pattern for data ingestion. In a clean instance of the starter pack, navigate to the `agentic_rag_vertex_ai_search` directory and execute the following command: + + ```bash + python app/patterns/agentic_rag_vertex_ai_search/pattern_setup/prepare_pattern.py + ``` + +2. **Setup Dev Terraform:** Follow the instructions in the parent [deployment/README.md - Dev Deployment section](../../../deployment/README.md#dev-deployment) to set up the development environment using Terraform. This will deploy a datastore and configure the necessary permissions in your development project. + + Refer to the [Terraform Variables section](#terraform-variables) to learn about the additional variables required for this pattern. + +3. **Test the Data Ingestion Pipeline:** + + After successfully deploying the Terraform infrastructure, you can test the data ingestion pipeline. This pipeline is responsible for loading, chunking, embedding, and importing your data into the Vertex AI Search datastore. + + > Note: The first pipeline execution may take additional time as your project is being configured to use Vertex AI Pipelines. + + **a. Navigate to the Data Processing Directory:** + + Change your working directory to the location of the data processing scripts: + + ```bash + cd data_processing + ``` + + **b. Install Dependencies:** + + Ensure you have the necessary Python dependencies installed by running: + + ```bash + poetry install + ``` + + **c. Execute the Pipeline:** + + Use the following command to execute the data ingestion pipeline. Replace the placeholder values with your actual project details: + + ```bash + PROJECT_ID="YOUR_PROJECT_ID" + REGION="us-central1" + REGION_VERTEX_AI_SEARCH="us" + poetry run python data_processing_pipeline/submit_pipeline.py \ + --project-id=$PROJECT_ID \ + --region=$REGION \ + --region-vertex-ai-search=$REGION_VERTEX_AI_SEARCH \ + --data-store-id="sample-datastore" \ + --service-account="vertexai-pipelines-sa@$PROJECT_ID.iam.gserviceaccount.com" \ + --pipeline-root="gs://$PROJECT_ID-pipeline-artifacts" \ + --pipeline-name="data-ingestion-pipeline" + ``` + + **Explanation of Parameters:** + + - `--project-id`: Your Google Cloud Project ID. + - `--region`: The region where Vertex AI Pipelines will be executed (e.g., `us-central1`). + - `--region-vertex-ai-search`: The region for Vertex AI Search operations (e.g., `us` or `eu`). + - `--data-store-id`: The ID of your Vertex AI Search data store. + - `--service-account`: The service account email used for pipeline execution. + - `--pipeline-root`: The GCS bucket name for storing pipeline artifacts. + - `--pipeline-name`: A display name for your pipeline. + - `--schedule-only`: _(Optional)_ If true, only schedules the pipeline without immediate execution. Must be used with `--cron-schedule`. + - `--cron-schedule`: _(Optional)_ A cron schedule to run the pipeline periodically (e.g., `"0 9 * * 1"` for every Monday at 9 AM UTC). + + **d. Pipeline Execution Behavior:** + + The pipeline executes immediately. Use the `--schedule-only` flag with a `cron_schedule` to only schedule the pipeline without immediate execution. If no schedule exists, one is created. If a schedule exists, its cron expression is updated. + + **e. Monitoring Pipeline Execution:** + + The pipeline will output its configuration and execution status to the console. For detailed monitoring, you can use the Vertex AI Pipeline dashboard in your Google Cloud Console. This dashboard provides insights into the pipeline's progress, logs, and any potential issues. + +4. **Test the Application in the Playground** + + You are now ready to test your RAG application with Vertex AI Search locally. + To do that you can follow the instructions in the [root readme](../../../README.md#installation). + + 1. Navigate to the root folder & install the required dependencies: + + ```bash + poetry install --with streamlit,jupyter + ``` + + 2. Configure your Google Cloud environment: + + ```bash + export PROJECT_ID="YOUR_PROJECT_ID" + gcloud config set project $PROJECT_ID + gcloud auth application-default login + gcloud auth application-default set-quota-project $PROJECT_ID + ``` + + 3. Check your [app/chain.py](../../../app/chain.py) file to understand its content and verify the datastore ID and region are configured properly. + + 4. Launch the playground: + + ```bash + make playground + ``` + + 5. Test your application! + + > **Note:** If you encounter the error `"google.api_core.exceptions.InvalidArgument: 400 The embedding field path: embedding not found in schema"` after the first ingestion, please wait a few minutes and try again. + +5. **Setup Staging and Production:** Once you've validated the setup in your development environment, proceed to set up the Terraform infrastructure for your staging and production environments. Follow the instructions provided in the [deployment/README.md](../../../deployment/README.md) to configure and deploy the necessary resources. + + This ensures that your data ingestion pipeline is integrated into your CI/CD workflow. Once the setup is complete, any commit to the main branch will trigger the pipeline, updating your Vertex AI Search application with the latest data, and deploying the updated application and tests. + + Your CI/CD pipeline is now configured - any new commits will trigger the data ingestion, testing and deployment process automatically. + +## Terraform Variables + +This pattern introduces the following additional Terraform variables: + +| Variable | Description | Default Value | +| --------------------------- | ---------------------------------------------------------------------------------- | ----------------------- | +| `search_engine_name` | The name of the Vertex AI Search engine. | `sample-search-engine` | +| `datastore_name` | The name of the Vertex AI Agent Builder Datastore. | `sample-datastore` | +| `vertexai_pipeline_sa_name` | The name of the service account used for Vertex AI Pipelines. | `vertexai-pipelines-sa` | +| `region_vertex_ai_search` | The region for the Vertex AI Search engine. Can be one of "global", "us", or "eu". | `us` | +| `pipeline_cron_schedule` | A cron expression defining the schedule for automated data ingestion. | `0 0 * * 0` | + +These variables are defined in the `deployment/terraform/variables.tf` and `deployment/terraform/dev/variables.tf` files and can be customized in your `deployment/terraform/vars/env.tfvars` and `deployment/terraform/dev/vars/env.tfvars` files respectively. diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/prepare_pattern.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/prepare_pattern.py new file mode 100644 index 00000000000..6e39d4e2e4d --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/prepare_pattern.py @@ -0,0 +1,134 @@ +# Copyright 2024 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 shutil +from pathlib import Path + + +def replace_in_file( + file_path: Path, search_str: str, replacement_file_path: Path +) -> None: + """Replace a string in a file with contents from another file.""" + with open(replacement_file_path, "r") as f: + replacement_content = f.read() + + with open(file_path, "r") as f: + content = f.read() + + with open(file_path, "w") as f: + f.write(content.replace(search_str, replacement_content)) + + +def append_file_contents(source_file: Path, target_file: Path) -> None: + """Append contents of one file to another.""" + with open(source_file, "r") as source, open(target_file, "a") as target: + target.write(source.read()) + + +def main() -> None: + """Set up the agentic RAG pattern by copying and updating Terraform files. + + Makes a backup of the deployment folder, then updates various Terraform files + with pattern-specific configurations by replacing content and appending updates. + """ + base_path = Path( + "app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy" + ) + terraform_path = base_path / "deployment/terraform" + deployment_path = Path("deployment/terraform") + # Make backup copy of deployment folder + deployment_backup_path = Path(".deployment_backup") + if deployment_backup_path.exists(): + shutil.rmtree(deployment_backup_path) + print(f"Creating backup of deployment folder at '{deployment_backup_path}'") + shutil.copytree("deployment", deployment_backup_path, dirs_exist_ok=True) + + # Replace content in build_triggers.tf + build_triggers_replacements = { + "# Your other CD Pipeline substitutions": terraform_path + / "substitute__cd_pipeline_triggers.tf_updates", + "# Your other Deploy to Prod Pipeline substitutions": terraform_path + / "substitute__deploy_to_prod_pipeline_triggers.tf_updates", + } + + for search_str, replacement_file in build_triggers_replacements.items(): + replace_in_file( + deployment_path / "build_triggers.tf", search_str, replacement_file + ) + + # Append contents to various tf files + tf_files_to_append = { + "iam.tf": "append__iam.tf_updates", + "service_accounts.tf": "append__service_accounts.tf_updates", + "storage.tf": "append__storage.tf_updates", + "variables.tf": "append__variables.tf_updates", + } + + for target_file, source_file in tf_files_to_append.items(): + append_file_contents( + terraform_path / source_file, deployment_path / target_file + ) + + # Append to env.tfvars + append_file_contents( + terraform_path / "vars/append__env.tfvars_updates", + deployment_path / "vars/env.tfvars", + ) + + # Copy files + shutil.copy( + terraform_path / "data_store.tf_updates", deployment_path / "data_store.tf" + ) + shutil.copytree(base_path / "deployment/cd", "deployment/cd", dirs_exist_ok=True) + # Additional operations on dev folder + # Define files to append in dev directory + dev_files_to_append = { + "dev/vars/env.tfvars": "dev/vars/append__env.tfvars_updates", + "dev/variables.tf": "dev/append__variables.tf_updates", + "dev/iam.tf": "dev/append__iam.tf_updates", + "dev/service_accounts.tf": "dev/append__service_accounts.tf_updates", + "dev/storage.tf": "dev/append__storage.tf_updates", + } + + # Append contents to each file + for target_file, source_file in dev_files_to_append.items(): + append_file_contents( + terraform_path / source_file, deployment_path / target_file + ) + shutil.copy( + terraform_path / "dev/data_store.tf_updates", + deployment_path / "dev/data_store.tf", + ) + + # Setup data ingestion + data_processing_path = Path("data_processing") + data_processing_path.mkdir(exist_ok=True) + shutil.copytree( + base_path / "data_processing", data_processing_path, dirs_exist_ok=True + ) + for filename in ("chain.py", "retrievers.py", "templates.py"): + shutil.copy(base_path / "app" / filename, Path("app") / filename) + + # Setup tests + test_integration_path = Path("tests/integration") + shutil.copy( + base_path / "tests/integration/test_chain.py", + test_integration_path / "test_chain.py", + ) + + print("Successfully copied pattern files") + + +if __name__ == "__main__": + main() diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/chain.py new file mode 100644 index 00000000000..8d9e7be253f --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/chain.py @@ -0,0 +1,139 @@ +# Copyright 2024 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. +# pylint: disable=E0611 + +import os +from typing import Dict, List + +import google +import vertexai + +from langchain_core.documents import Document +from langchain_core.messages import BaseMessage +from langchain_core.runnables import RunnableConfig +from langchain_core.tools import tool +from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings +from langgraph.graph import END, MessagesState, StateGraph +from langgraph.prebuilt import ToolNode +from app.templates import rag_template, format_docs, inspect_conversation_template +from app.retrievers import get_retriever, get_compressor + +EMBEDDING_MODEL = "text-embedding-004" +LLM_MODEL = "gemini-1.5-flash-002" +TOP_K = 5 + +VERTEX_AI_LOCATION = os.getenv("REGION", "us-central1") +AGENT_BUILDER_LOCATION = os.getenv("AGENT_BUILDER_LOCATION", "us") +DATA_STORE_ID = os.getenv("DATA_STORE_ID", "sample-datastore") + +# Initialize Google Cloud and Vertex AI +credentials, project_id = google.auth.default() +vertexai.init(project=project_id) + +embedding = VertexAIEmbeddings(model_name=EMBEDDING_MODEL, location=VERTEX_AI_LOCATION) + +retriever = get_retriever( + project_id=project_id, + data_store_id=DATA_STORE_ID, + agent_builder_location=AGENT_BUILDER_LOCATION, + embedding=embedding, +) +compressor = get_compressor(project_id=project_id) + + +@tool(response_format="content_and_artifact") +def retrieve_docs(query: str) -> tuple[str, List[Document]]: + """ + Useful for retrieving relevant documents based on a query. + Use this when you need additional information to answer a question. + + Args: + query (str): The user's question or search query. + + Returns: + List[Document]: A list of the top-ranked Document objects, limited to TOP_K (5) results. + """ + # Use the retriever to fetch relevant documents based on the query + retrieved_docs = retriever.invoke(query) + # Re-rank docs with Vertex AI Rank for better relevance + ranked_docs = compressor.compress_documents(documents=retrieved_docs, query=query) + # Format ranked documents into a consistent structure for LLM consumption + formatted_docs = format_docs.format(docs=ranked_docs) + return (formatted_docs, ranked_docs) + + +@tool +def should_continue() -> None: + """ + Use this tool if you determine that you have enough context to respond to the questions of the user. + """ + return None + + +tools = [retrieve_docs, should_continue] + +llm = ChatVertexAI(model=LLM_MODEL, temperature=0, max_tokens=1024, streaming=True) + +# Set up conversation inspector +inspect_conversation = inspect_conversation_template | llm.bind_tools( + tools, tool_choice="any" +) + +# Set up response chain +response_chain = rag_template | llm + + +def inspect_conversation_node( + state: MessagesState, config: RunnableConfig +) -> Dict[str, BaseMessage]: + """Inspects the conversation state and returns the next message using the conversation inspector.""" + response = inspect_conversation.invoke(state, config) + return {"messages": response} + + +def generate_node( + state: MessagesState, config: RunnableConfig +) -> Dict[str, BaseMessage]: + """Generates a response using the RAG template and returns it as a message.""" + response = response_chain.invoke(state, config) + return {"messages": response} + + +# Flow: +# 1. Start with agent node that inspects conversation using inspect_conversation_node +# 2. Agent node connects to tools node which can either: +# - Retrieve relevant docs using retrieve_docs tool +# - End tool usage with should_continue tool +# 3. Tools node connects to generate node which produces final response +# 4. Generate node connects to END to complete the workflow + +workflow = StateGraph(MessagesState) +workflow.add_node("agent", inspect_conversation_node) +workflow.add_node("generate", generate_node) +workflow.set_entry_point("agent") + +workflow.add_node( + "tools", + ToolNode( + tools=tools, + # With False, tool errors won't be caught by LangGraph + handle_tool_errors=False, + ), +) +workflow.add_edge("agent", "tools") +workflow.add_edge("tools", "generate") + +workflow.add_edge("generate", END) + +chain = workflow.compile() diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/retrievers.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/retrievers.py new file mode 100644 index 00000000000..6948794f61e --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/retrievers.py @@ -0,0 +1,74 @@ +# Copyright 2024 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 os +from unittest.mock import MagicMock +from langchain_google_community import VertexAISearchRetriever +from langchain_google_community.vertex_rank import VertexAIRank +from langchain_google_vertexai import VertexAIEmbeddings + + +def get_retriever( + project_id: str, + data_store_id: str, + agent_builder_location: str, + embedding: VertexAIEmbeddings, +) -> VertexAISearchRetriever: + """ + Creates and returns an instance of the retriever service. + + Uses mock service if the INTEGRATION_TEST environment variable is set to "TRUE", + otherwise initializes real Vertex AI retriever. + """ + if os.getenv("INTEGRATION_TEST") == "TRUE": + retriever = MagicMock() + retriever.invoke = lambda x: [] + return retriever + + return VertexAISearchRetriever( + project_id=project_id, + data_store_id=data_store_id, + location_id=agent_builder_location, + engine_data_type=1, + # The following parameters are used when you want to search + # using custom embeddings in Agent Builder. + # The ratio is set to 0.5 by default to use a mix of custom + # embeddings but you can adapt the ratio as you need. + custom_embedding_ratio=0.5, + custom_embedding=embedding, + custom_embedding_field_path="embedding", + # Extracting 20 documents before re-rank. + max_documents=20, + ) + + +def get_compressor(project_id: str) -> VertexAIRank: + """ + Creates and returns an instance of the compressor service. + + Uses mock service if the INTEGRATION_TEST environment variable is set to "TRUE", + otherwise initializes real Vertex AI compressor. + """ + if os.getenv("INTEGRATION_TEST") == "TRUE": + compressor = MagicMock() + compressor.compress_documents = lambda documents, query: [] + return compressor + + return VertexAIRank( + project_id=project_id, + location_id="global", + ranking_config="default_ranking_config", + title_field="id", + top_n=5, + ) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/templates.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/templates.py new file mode 100644 index 00000000000..a289f282cfa --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/app/templates.py @@ -0,0 +1,53 @@ +# Copyright 2024 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. + +from langchain_core.prompts import ( + PromptTemplate, + ChatPromptTemplate, + MessagesPlaceholder, +) + +format_docs = PromptTemplate.from_template( + """## Context provided: +{% for doc in docs%} + +{{ doc.page_content | safe }} + +{% endfor %} +""", + template_format="jinja2", +) + +inspect_conversation_template = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are an AI assistant tasked with analyzing the conversation""" + """ and determining the best course of action.""", + ), + MessagesPlaceholder(variable_name="messages"), + ] +) + +rag_template = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are an AI assistant for question-answering tasks.""" + """ Answer to the best of your ability using the context provided.""" + """ Leverage the Tools you are provided to answer questions.""", + ), + MessagesPlaceholder(variable_name="messages"), + ] +) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/README.md new file mode 100644 index 00000000000..c4937c11fca --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/README.md @@ -0,0 +1,7 @@ +# Ingestion Pipeline + +This pipeline is designed to automate the ingestion of data into Vertex AI Search (Agent Builder Search) for use in Retrieval Augmented Generation (RAG) applications. It handles the complete workflow of loading documents, chunking them into appropriate segments, generating embeddings using Vertex AI Embeddings, and importing the processed data into your Vertex AI Search datastore. + +The pipeline can be triggered as a one-time execution for initial data loading or scheduled to run periodically using a cron schedule for keeping your search index up-to-date. It leverages Vertex AI Pipelines for orchestration & monitoring. + +For detailed step-by-step instructions on setting up and running this pipeline, please refer to the [Agentic RAG with Vertex AI Search Pattern Guide](../app/patterns/agentic_rag_vertex_ai_search/README.md). diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/components.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/components.py new file mode 100644 index 00000000000..f372151b533 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/components.py @@ -0,0 +1,270 @@ +# Copyright 2024 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. +# pylint: disable=C0415,R0917,R0913,R0914 + +from typing import Optional +from kfp.dsl import component, Dataset, Output, Input + + +@component( + packages_to_install=[ + "langchain", + "langchain-community", + "vertexai", + "langchain-google-vertexai", + "pypdf", + "pydantic==2.9.2", + ] +) +def process_data( + output_files: Output[Dataset], + embedding_model: str, + pdf_url: str, +) -> None: + """Processes PDF document by splitting into chunks and generating embeddings.""" + import json + import logging + import uuid + import vertexai + from langchain.text_splitter import RecursiveCharacterTextSplitter + from langchain_community.document_loaders import PyPDFLoader + from langchain_core.documents import Document + from langchain_core.embeddings import Embeddings + from langchain_google_vertexai import VertexAIEmbeddings + from typing import List + + vertexai.init() + embedding = VertexAIEmbeddings(model_name=embedding_model) + + def pre_process_data(url: str) -> List[Document]: + """Load and split documents from a given URL.""" + loader = PyPDFLoader(url) + documents = loader.load() + + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=1000, chunk_overlap=200 + ) + doc_splits = text_splitter.split_documents(documents) + for document in doc_splits: + document.metadata["title"] = pdf_url + document.metadata["id"] = str(uuid.uuid4()) + + return doc_splits + + def add_embeddings(docs: List[Document], embedding: Embeddings) -> List[Document]: + """Adds embeddings to a list of documents using the provided embedding model.""" + embeddings = embedding.embed_documents( + [ + f"{document.metadata['title']}\n{document.page_content}" + for document in docs + ] + ) + + documents_with_embeddings = [] + + for index, current_document in enumerate(docs): + current_document.metadata["embedding"] = embeddings[index] + documents_with_embeddings.append(current_document) + + return documents_with_embeddings + + def convert_docs_to_jsonl(docs: List[Document], file_path: str) -> None: + """Converts an array of documents to a jsonl file and stores it in GCS. + + Args: + docs: An array of documents, where each document is a dictionary. + """ + with open(file_path, "w") as f: + for doc in docs: + json_data = doc.metadata + json_data["content"] = doc.page_content + dictionary = { + "id": doc.metadata["id"], + "json_data": json.dumps(json_data), + } + f.write(json.dumps(dictionary) + "\n") + + logging.info("Starting document pre-processing...") + docs = pre_process_data(pdf_url) + logging.info(f"Pre-processed {len(docs)} document chunks") + + logging.info("Generating embeddings...") + docs = add_embeddings(docs, embedding) + logging.info("Embeddings generated successfully") + + logging.info("Converting documents to JSONL format...") + output_files.path = output_files.path + ".json" + convert_docs_to_jsonl(docs, output_files.path) + logging.info("JSONL conversion complete") + + +@component( + packages_to_install=[ + "google-cloud-discoveryengine", + ], +) +def ingest_data_in_datastore( + project_id: str, + region_vertex_ai_search: str, + input_files: Input[Dataset], + data_store_id: str, + embedding_dimension: int = 768, + embedding_column: str = "embedding", +) -> None: + """Process and ingest documents into Vertex AI Search datastore. + + Args: + project_id: Google Cloud project ID + region_vertex_ai_search: Region for Vertex AI Search + input_files: Input dataset containing documents + data_store_id: ID of target datastore + embedding_column: Name of embedding column in schema + """ + import json + import logging + from google.api_core.client_options import ClientOptions + from google.cloud import discoveryengine + + def update_schema_as_json( + original_schema: str, + embedding_dimension: int, + field_name: Optional[str] = None, + ) -> str: + """Update datastore schema JSON to include embedding field. + + Args: + original_schema: Original schema JSON string + field_name: Name of embedding field to add + + Returns: + Updated schema JSON string + """ + original_schema_dict = json.loads(original_schema) + + if original_schema_dict.get("properties") is None: + original_schema_dict["properties"] = {} + + if field_name: + field_schema = { + "type": "array", + "keyPropertyMapping": "embedding_vector", + "dimension": embedding_dimension, + "items": {"type": "number"}, + } + original_schema_dict["properties"][field_name] = field_schema + + return json.dumps(original_schema_dict) + + def update_data_store_schema( + project_id: str, + location: str, + data_store_id: str, + field_name: Optional[str] = None, + client_options: Optional[ClientOptions] = None, + ) -> None: + """Update datastore schema to include embedding field. + + Args: + project_id: Google Cloud project ID + location: Google Cloud location + data_store_id: Target datastore ID + embedding_column: Name of embedding column + client_options: Client options for API + """ + schema_client = discoveryengine.SchemaServiceClient( + client_options=client_options + ) + collection = "default_collection" + + name = f"projects/{project_id}/locations/{location}/collections/{collection}/dataStores/{data_store_id}/schemas/default_schema" + + schema = schema_client.get_schema( + request=discoveryengine.GetSchemaRequest(name=name) + ) + new_schema_json = update_schema_as_json( + original_schema=schema.json_schema, + embedding_dimension=embedding_dimension, + field_name=field_name, + ) + new_schema = discoveryengine.Schema(json_schema=new_schema_json, name=name) + + operation = schema_client.update_schema( + request=discoveryengine.UpdateSchemaRequest( + schema=new_schema, allow_missing=True + ) + ) + logging.info(f"Waiting for schema update operation: {operation.operation.name}") + operation.result() + + def add_data_in_store( + project_id: str, + location: str, + data_store_id: str, + input_files_uri: str, + client_options: Optional[ClientOptions] = None, + ) -> None: + """Import documents into datastore. + + Args: + project_id: Google Cloud project ID + location: Google Cloud location + data_store_id: Target datastore ID + input_files_uri: URI of input files + client_options: Client options for API + """ + client = discoveryengine.DocumentServiceClient(client_options=client_options) + + parent = client.branch_path( + project=project_id, + location=location, + data_store=data_store_id, + branch="default_branch", + ) + + request = discoveryengine.ImportDocumentsRequest( + parent=parent, + gcs_source=discoveryengine.GcsSource( + input_uris=[input_files_uri], + data_schema="document", + ), + reconciliation_mode=discoveryengine.ImportDocumentsRequest.ReconciliationMode.FULL, + ) + + operation = client.import_documents(request=request) + logging.info(f"Waiting for import operation: {operation.operation.name}") + operation.result() + + client_options = ClientOptions( + api_endpoint=f"{region_vertex_ai_search}-discoveryengine.googleapis.com" + ) + + logging.info("Updating data store schema...") + update_data_store_schema( + project_id=project_id, + location=region_vertex_ai_search, + data_store_id=data_store_id, + field_name=embedding_column, + client_options=client_options, + ) + logging.info("Schema updated successfully") + + logging.info("Importing data into store...") + add_data_in_store( + project_id=project_id, + location=region_vertex_ai_search, + data_store_id=data_store_id, + client_options=client_options, + input_files_uri=input_files.uri, + ) + logging.info("Data import completed") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/pipeline.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/pipeline.py new file mode 100644 index 00000000000..a3f9bf01bb0 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/pipeline.py @@ -0,0 +1,38 @@ +# Copyright 2024 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. + +from kfp import dsl +from data_processing_pipeline.components import process_data, ingest_data_in_datastore + + +@dsl.pipeline(description="A pipeline to run ingestion of new data into the datastore") +def pipeline( + project_id: str, + region_vertex_ai_search: str, + data_store_id: str, + embedding_model: str = "text-embedding-004", + pdf_url: str = "https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf", +) -> None: + """Processes a PDF document and ingests it into Vertex AI Search datastore.""" + + # Process the PDF document and generate embeddings + processed_data = process_data(embedding_model=embedding_model, pdf_url=pdf_url) + + # Ingest the processed data into Vertex AI Search datastore + ingest_data_in_datastore( + project_id=project_id, + region_vertex_ai_search=region_vertex_ai_search, + input_files=processed_data.output, + data_store_id=data_store_id, + ) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/submit_pipeline.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/submit_pipeline.py new file mode 100644 index 00000000000..9cf9f3c8b75 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/data_processing_pipeline/submit_pipeline.py @@ -0,0 +1,188 @@ +# Copyright 2024 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 os +import argparse +import sys +from google.cloud import aiplatform +from kfp import compiler +from data_processing_pipeline.pipeline import pipeline + + +PIPELINE_FILE_NAME = "data_processing_pipeline.json" + + +def parse_args() -> argparse.Namespace: + """Parse command line arguments for pipeline configuration.""" + + parser = argparse.ArgumentParser(description="Pipeline configuration") + parser.add_argument( + "--project-id", default=os.getenv("PROJECT_ID"), help="GCP Project ID" + ) + parser.add_argument( + "--region", default=os.getenv("REGION"), help="Vertex AI Pipelines region" + ) + parser.add_argument( + "--region-vertex-ai-search", + default=os.getenv("REGION_VERTEX_AI_SEARCH"), + help="Vertex AI Search region", + ) + parser.add_argument( + "--data-store-id", default=os.getenv("DATA_STORE_ID"), help="Data store ID" + ) + parser.add_argument( + "--service-account", + default=os.getenv("SERVICE_ACCOUNT"), + help="Service account", + ) + parser.add_argument( + "--pipeline-root", + default=os.getenv("PIPELINE_ROOT"), + help="Pipeline root directory", + ) + parser.add_argument( + "--pipeline-name", default=os.getenv("PIPELINE_NAME"), help="Pipeline name" + ) + parser.add_argument( + "--disable-caching", + type=bool, + default=os.getenv("DISABLE_CACHING", "false").lower() == "true", + help="Enable pipeline caching", + ) + parser.add_argument( + "--cron-schedule", + default=os.getenv("CRON_SCHEDULE", None), + help="Cron schedule", + ) + parser.add_argument( + "--schedule-only", + type=bool, + default=os.getenv("SCHEDULE_ONLY", "false").lower() == "true", + help="Schedule only (do not submit)", + ) + parsed_args = parser.parse_args() + + # Validate required parameters + missing_params = [] + required_params = { + "project_id": parsed_args.project_id, + "region": parsed_args.region, + "region_vertex_ai_search": parsed_args.region_vertex_ai_search, + "data_store_id": parsed_args.data_store_id, + "service_account": parsed_args.service_account, + "pipeline_root": parsed_args.pipeline_root, + "pipeline_name": parsed_args.pipeline_name, + } + + for param_name, param_value in required_params.items(): + if param_value is None: + missing_params.append(param_name) + + if missing_params: + print("Error: The following required parameters are missing:") + for param in missing_params: + print(f" - {param}") + print( + "\nPlease provide these parameters either through environment variables or command line arguments." + ) + sys.exit(1) + + return parsed_args + + +if __name__ == "__main__": + args = parse_args() + + if args.schedule_only and not args.cron_schedule: + print("Missing --cron-schedule argument for scheduling") + sys.exit(1) + + # Print configuration + print("\nConfiguration:") + print("--------------") + print(f"project_id: {args.project_id}") + print(f"region: {args.region}") + print(f"region_search: {args.region_vertex_ai_search}") + print(f"data_store_id: {args.data_store_id}") + print(f"service_account: {args.service_account}") + print(f"pipeline_root: {args.pipeline_root}") + print(f"cron_schedule: {args.cron_schedule}") + print(f"pipeline_name: {args.pipeline_name}") + print(f"disable_caching: {args.disable_caching}") + print(f"schedule_only: {args.schedule_only}") + print("--------------\n") + + compiler.Compiler().compile(pipeline_func=pipeline, package_path=PIPELINE_FILE_NAME) + + if not args.schedule_only: + job = aiplatform.PipelineJob( + display_name=args.pipeline_name, + template_path=PIPELINE_FILE_NAME, + pipeline_root=args.pipeline_root, + project=args.project_id, + enable_caching=(not args.disable_caching), + location=args.region, + parameter_values={ + "project_id": args.project_id, + "region_vertex_ai_search": args.region_vertex_ai_search, + "data_store_id": args.data_store_id, + }, + ) + print("Running pipeline and waiting for completion...") + job.submit(service_account=args.service_account) + job.wait() + print("Pipeline completed!") + + if args.cron_schedule: + if args.schedule_only: + job = aiplatform.PipelineJob( + display_name=args.pipeline_name, + template_path=PIPELINE_FILE_NAME, + pipeline_root=args.pipeline_root, + project=args.project_id, + enable_caching=(not args.disable_caching), + location=args.region, + parameter_values={ + "project_id": args.project_id, + "region_vertex_ai_search": args.region_vertex_ai_search, + "data_store_id": args.data_store_id, + }, + ) + pipeline_job_schedule = aiplatform.PipelineJobSchedule( + pipeline_job=job, + display_name=f"{args.pipeline_name} Weekly Ingestion Job", + credentials=job.credentials, + project=job.project, + location=job.location, + ) + + schedule_list = pipeline_job_schedule.list( + filter=f'display_name="{args.pipeline_name} Weekly Ingestion Job"', + project=args.project_id, + location=args.region, + ) + print("Schedule lists found: ", schedule_list) + if not schedule_list: + pipeline_job_schedule.create( + cron=args.cron_schedule, service_account=args.service_account + ) + print("Schedule created") + else: + schedule_list[0].update(cron=args.cron_schedule) + print("Schedule updated") + + # Clean up pipeline file + if os.path.exists(PIPELINE_FILE_NAME): + os.remove(PIPELINE_FILE_NAME) + print(f"Deleted {PIPELINE_FILE_NAME}") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/poetry.lock b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/poetry.lock new file mode 100644 index 00000000000..7a1249b2ddc --- /dev/null +++ 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b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/data_processing/pyproject.toml @@ -0,0 +1,15 @@ +[tool.poetry] +name = "data-processing-pipeline" +version = "0.1.0" +description = "" +authors = ["Your Name "] + +[tool.poetry.dependencies] +python = "^3.10" +kfp = "^2.10.1" +google-cloud-aiplatform = "^1.72.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/cd/deploy-to-prod.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/cd/deploy-to-prod.yaml new file mode 100644 index 00000000000..5195e8c1074 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/cd/deploy-to-prod.yaml @@ -0,0 +1,63 @@ +steps: + - name: "python:3.10" + id: deploy-data-ingestion-pipeline-prod + entrypoint: bash + args: + - -c + - | + cd data_processing && pip install poetry==1.8.3 --user && python -m poetry install && \ + python -m poetry run python data_processing_pipeline/submit_pipeline.py + env: + - "PIPELINE_ROOT=${_PIPELINE_GCS_ROOT}" + - "REGION=${_REGION}" + - "REGION_VERTEX_AI_SEARCH=${_REGION_VERTEX_AI_SEARCH}" + - "DATA_STORE_ID=${_DATA_STORE_ID}" + - "PROJECT_ID=${_PROD_PROJECT_ID}" + - "SERVICE_ACCOUNT=${_PIPELINE_SA_EMAIL}" + - "PIPELINE_NAME=${_PIPELINE_NAME}" + - "CRON_SCHEDULE=${_PIPELINE_CRON_SCHEDULE}" + - "DISABLE_CACHING=TRUE" + + - name: "gcr.io/cloud-builders/gcloud" + id: trigger-deployment + entrypoint: gcloud + args: + - "run" + - "deploy" + - "genai-app-sample" + - "--image" + - "$_REGION-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME" + - "--region" + - "$_REGION" + - "--project" + - $_PROD_PROJECT_ID + - "--min-instances" + - "1" + - "--no-cpu-throttling" + - "--cpu" + - "4" + - "--memory" + - "4Gi" + - "--concurrency" + - "40" + - "--service-account" + - "${_CLOUD_RUN_APP_SA_NAME}@${_PROD_PROJECT_ID}.iam.gserviceaccount.com" + - "--set-env-vars" + - "COMMIT_SHA=${COMMIT_SHA},DATA_STORE_ID=${_DATA_STORE_ID},REGION=${_REGION},REGION_VERTEX_AI_SEARCH=${_REGION_VERTEX_AI_SEARCH}" + +substitutions: + _PROD_PROJECT_ID: YOUR_PROD_PROJECT_ID + _CONTAINER_NAME: genai-app-sample + _ARTIFACT_REGISTRY_REPO_NAME: genai-containers + _CLOUD_RUN_APP_SA_NAME: genai-app-sample-cr-sa + _REGION: us-central1 + + _REGION_VERTEX_AI_SEARCH: us + _PIPELINE_GCS_ROOT: _PIPELINE_GCS_ROOT + _PIPELINE_SA_EMAIL: YOUR_PIPELINE_SA_EMAIL + _DATA_STORE_ID: PROD_DATASTORE_ID + _PIPELINE_NAME: genai_sample_data_processing + _PIPELINE_CRON_SCHEDULE: YOUR_CRON_SCHEDULE 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"REGION_VERTEX_AI_SEARCH=${_REGION_VERTEX_AI_SEARCH}" + - "DATA_STORE_ID=${_DATA_STORE_ID}" + - "PROJECT_ID=${_STAGING_PROJECT_ID}" + - "SERVICE_ACCOUNT=${_PIPELINE_SA_EMAIL}" + - "PIPELINE_NAME=${_PIPELINE_NAME}" + + - name: "gcr.io/cloud-builders/docker" + id: build-backend-service-container + args: + [ + "build", + "-t", + "$_REGION-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME", + ".", + ] + + - name: "gcr.io/cloud-builders/docker" + args: + [ + "push", + "$_REGION-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME", + ] + + # Deploy to Staging + - name: "gcr.io/cloud-builders/gcloud" + id: deploy-staging + entrypoint: gcloud + args: + - "run" + - "deploy" + - "genai-app-sample" + - "--image" + - "$_REGION-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME" + - "--region" + - "${_REGION}" + - "--project" + - "${_STAGING_PROJECT_ID}" + - "--min-instances" + - "1" + - "--no-cpu-throttling" + - "--cpu" + - "4" + - "--memory" + - "4Gi" + - "--concurrency" + - "40" + - "--service-account" + - "${_CLOUD_RUN_APP_SA_NAME}@${_STAGING_PROJECT_ID}.iam.gserviceaccount.com" + - "--set-env-vars" + - "COMMIT_SHA=${COMMIT_SHA},DATA_STORE_ID=${_DATA_STORE_ID},REGION=${_REGION},REGION_VERTEX_AI_SEARCH=${_REGION_VERTEX_AI_SEARCH}" + + # Fetch Staging Service URL + - name: "gcr.io/cloud-builders/gcloud" + id: fetch-staging-url + entrypoint: /bin/bash + args: + - "-c" + - | + echo $(gcloud run services describe genai-app-sample \ + --region ${_REGION} --project ${_STAGING_PROJECT_ID} --format="value(status.url)") > staging_url.txt + + # Fetch ID Token + - name: gcr.io/cloud-builders/gcloud + id: fetch-id-token + entrypoint: /bin/bash + args: + - "-c" + - | + echo $(gcloud auth print-identity-token -q) > id_token.txt + + # Load Testing + - name: "python:3.10" + id: load_test + entrypoint: /bin/bash + args: + - "-c" + - | + export _ID_TOKEN=$(cat id_token.txt) + export _STAGING_URL=$(cat staging_url.txt) + pip install locust==2.31.1 + python -m locust -f tests/load_test/load_test.py \ + -H $$_STAGING_URL \ + --headless \ + -t 30s -u 10 -r 0.5 \ + --csv=tests/load_test/.results/results \ + --html=tests/load_test/.results/report.html + + # Export Load Test Results to GCS + - name: gcr.io/cloud-builders/gcloud + id: export-results-to-gcs + entrypoint: /bin/bash + args: + - "-c" + - | + export _TIMESTAMP=$(date +%Y%m%d-%H%M%S) + gsutil -m cp -r tests/load_test/.results gs://${_BUCKET_NAME_LOAD_TEST_RESULTS}/results-$${_TIMESTAMP} + echo "_________________________________________________________________________" + echo "Load test results copied to gs://${_BUCKET_NAME_LOAD_TEST_RESULTS}/results-$${_TIMESTAMP}" + echo "HTTP link: https://console.cloud.google.com/storage/browser/${_BUCKET_NAME_LOAD_TEST_RESULTS}/results-$${_TIMESTAMP}" + echo "_________________________________________________________________________" + + # # Trigger Prod Deployment + - name: gcr.io/cloud-builders/gcloud + id: trigger-prod-deployment + entrypoint: gcloud + args: + - "beta" + - "builds" + - "triggers" + - "run" + - "deploy-to-prod-pipeline" + - "--region" + - "$_REGION" + - "--project" + - "$PROJECT_ID" + - "--substitutions" + - "_CONTAINER_NAME=${_CONTAINER_NAME}" + - "--sha" + - $COMMIT_SHA + + - name: gcr.io/cloud-builders/gcloud + id: echo-view-build-trigger-link + entrypoint: /bin/bash + args: + - "-c" + - | + echo "_________________________________________________________________________" + echo "Production deployment triggered. View progress and / or approve on the Cloud Build Console:" + echo "https://console.cloud.google.com/cloud-build/builds;region=$_REGION" + echo "_________________________________________________________________________" + +substitutions: + _STAGING_PROJECT_ID: YOUR_STAGING_PROJECT_ID # Replace with your staging project ID + _BUCKET_NAME_LOAD_TEST_RESULTS: YOUR_CICD_PROJECT_ID-cicd-load-test-results # Replace with your GCS bucket name + _CONTAINER_NAME: genai-app-sample + _ARTIFACT_REGISTRY_REPO_NAME: genai-containers + _CLOUD_RUN_APP_SA_NAME: genai-app-sample-cr-sa + _REGION: us-central1 + + _REGION_VERTEX_AI_SEARCH: us + _PIPELINE_GCS_ROOT: _PIPELINE_GCS_ROOT + _PIPELINE_SA_EMAIL: YOUR_PIPELINE_SA_EMAIL + _DATA_STORE_ID: STAGING_DATASTORE_ID + _PIPELINE_NAME: genai_sample_data_processing + _PIPELINE_CRON_SCHEDULE: YOUR_CRON_SCHEDULE + +options: + substitutionOption: ALLOW_LOOSE + defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET +serviceAccount: projects/${PROJECT_ID}/serviceAccounts/cicd-runner@${PROJECT_ID}.iam.gserviceaccount.com diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__iam.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__iam.tf_updates new file mode 100644 index 00000000000..8cccab9ee67 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__iam.tf_updates @@ -0,0 +1,17 @@ + +# ! Specific to Vertex AI Search pattern +# 5. Grant Vertex AI SA the required permissions to run the ingestion +resource "google_project_iam_member" "vertexai_pipeline_sa_roles" { + for_each = { + for pair in setproduct(keys(local.deploy_project_ids), var.pipelines_roles) : + join(",", pair) => { + project = local.deploy_project_ids[pair[0]] + role = pair[1] + } + } + + project = each.value.project + role = each.value.role + member = "serviceAccount:${google_service_account.vertexai_pipeline_app_sa[split(",", each.key)[0]].email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__service_accounts.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__service_accounts.tf_updates new file mode 100644 index 00000000000..f40f73908eb --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__service_accounts.tf_updates @@ -0,0 +1,11 @@ + +# Service account to run Vertex AI pipeline +# ! Specific to Vertex AI Search pattern +resource "google_service_account" "vertexai_pipeline_app_sa" { + for_each = local.deploy_project_ids + + account_id = var.vertexai_pipeline_sa_name + display_name = "Vertex AI Pipeline app SA" + project = each.value + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__storage.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__storage.tf_updates new file mode 100644 index 00000000000..5875273c0d4 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__storage.tf_updates @@ -0,0 +1,11 @@ +# ! Specific to Vertex AI Search pattern +resource "google_storage_bucket" "data_ingestion_pipeline_gcs_root" { + for_each = local.deploy_project_ids + name = "${each.value}-pipeline-artifacts" + location = var.region + project = each.value + uniform_bucket_level_access = true + force_destroy = true + + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__variables.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__variables.tf_updates new file mode 100644 index 00000000000..f3b21ea4abe --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/append__variables.tf_updates @@ -0,0 +1,44 @@ +# ! Specific to Vertex AI Search pattern + +variable "vertexai_pipeline_sa_name" { + description = "Service account name to be used for the Vertex AI service" + type = string + default = "data-ingestion-vertexai-sa" +} + +variable "pipeline_cron_schedule" { + type = string + description = "Cron expression defining the schedule for automated data ingestion." + default = "0 0 * * 0" # Run at 00:00 UTC every Sunday +} + +variable "region_vertex_ai_search" { + type = string + description = "Google Cloud region for resource deployment." + default = "us" +} + +variable "pipelines_roles" { + description = "List of roles to assign to the Vertex AI Pipelines service account" + type = list(string) + default = [ + "roles/storage.admin", + "roles/aiplatform.user", + "roles/discoveryengine.admin", + "roles/logging.logWriter", + "roles/artifactregistry.writer", + ] +} + +variable "datastore_name" { + description = "The name of the datastore" + type = string + default = "sample-datastore" +} + +variable "search_engine_name" { + description = "The name of the search engine" + type = string + default = "sample-search-engine" +} + diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/data_store.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/data_store.tf_updates new file mode 100644 index 00000000000..8a51257b15c --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/data_store.tf_updates @@ -0,0 +1,66 @@ +# ! Specific to Vertex AI Search pattern +# ------------------------------------- + +provider "google" { + alias = "staging_billing_override" + billing_project = var.staging_project_id + user_project_override = true +} + +provider "google" { + alias = "prod_billing_override" + billing_project = var.prod_project_id + user_project_override = true +} + +resource "google_discovery_engine_data_store" "data_store_staging" { + location = var.region_vertex_ai_search + project = var.staging_project_id + data_store_id = "${var.datastore_name}" + display_name = "${var.datastore_name}" + industry_vertical = "GENERIC" + content_config = "NO_CONTENT" + solution_types = ["SOLUTION_TYPE_SEARCH"] + create_advanced_site_search = false + provider = google.staging_billing_override + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +resource "google_discovery_engine_search_engine" "search_engine_staging" { + project = var.staging_project_id + engine_id = "${var.search_engine_name}" + collection_id = "default_collection" + location = google_discovery_engine_data_store.data_store_staging.location + display_name = "Search Engine App Staging" + data_store_ids = [google_discovery_engine_data_store.data_store_staging.data_store_id] + search_engine_config { + search_tier = "SEARCH_TIER_ENTERPRISE" + } + provider = google.staging_billing_override +} + +resource "google_discovery_engine_data_store" "data_store_prod" { + location = var.region_vertex_ai_search + project = var.prod_project_id + data_store_id = "${var.datastore_name}" + display_name = "${var.datastore_name}" + industry_vertical = "GENERIC" + content_config = "NO_CONTENT" + solution_types = ["SOLUTION_TYPE_SEARCH"] + create_advanced_site_search = false + provider = google.prod_billing_override + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +resource "google_discovery_engine_search_engine" "search_engine_prod" { + project = var.prod_project_id + engine_id = "${var.search_engine_name}" + collection_id = "default_collection" + location = google_discovery_engine_data_store.data_store_prod.location + display_name = "Search Engine App Prod" + data_store_ids = [google_discovery_engine_data_store.data_store_prod.data_store_id] + search_engine_config { + search_tier = "SEARCH_TIER_ENTERPRISE" + } + provider = google.prod_billing_override +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__iam.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__iam.tf_updates new file mode 100644 index 00000000000..be0e3661c84 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__iam.tf_updates @@ -0,0 +1,15 @@ +# ! Specific to Vertex AI Search pattern +resource "google_project_iam_member" "vertexai_pipeline_sa_roles" { + for_each = { + for pair in setproduct(keys(local.project_ids), var.pipelines_roles) : + join(",", pair) => { + project = local.project_ids[pair[0]] + role = pair[1] + } + } + + project = each.value.project + role = each.value.role + member = "serviceAccount:${google_service_account.vertexai_pipeline_app_sa[split(",", each.key)[0]].email}" + depends_on = [resource.google_project_service.services] +} \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__service_accounts.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__service_accounts.tf_updates new file mode 100644 index 00000000000..23d671208c6 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__service_accounts.tf_updates @@ -0,0 +1,12 @@ + +# ! Specific to Vertex AI Search pattern + +# Service account to run Vertex AI pipeline +resource "google_service_account" "vertexai_pipeline_app_sa" { + for_each = local.project_ids + + account_id = var.vertexai_pipeline_sa_name + display_name = "Vertex AI Pipeline app SA" + project = each.value + depends_on = [resource.google_project_service.services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__storage.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__storage.tf_updates new file mode 100644 index 00000000000..a5de3cbdf29 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__storage.tf_updates @@ -0,0 +1,10 @@ +# ! Specific to Vertex AI Search pattern +resource "google_storage_bucket" "data_ingestion_PIPELINE_GCS_ROOT" { + name = "${var.dev_project_id}-pipeline-artifacts" + location = var.region + project = var.dev_project_id + uniform_bucket_level_access = true + force_destroy = true + + depends_on = [resource.google_project_service.services] +} \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__variables.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__variables.tf_updates new file mode 100644 index 00000000000..656629949de --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/append__variables.tf_updates @@ -0,0 +1,39 @@ + +# ! Specific to Vertex AI Search pattern + +variable "vertexai_pipeline_sa_name" { + description = "Service account name to be used for the Vertex AI service" + type = string + default = "data-ingestion-vertexai-sa" +} + +variable "region_vertex_ai_search" { + type = string + description = "Google Cloud region for resource deployment." + default = "us" +} + +variable "pipelines_roles" { + description = "List of roles to assign to the Vertex AI runner service account" + type = list(string) + default = [ + "roles/storage.admin", + "roles/run.invoker", + "roles/aiplatform.user", + "roles/discoveryengine.admin", + "roles/logging.logWriter", + "roles/artifactregistry.writer", + ] +} + +variable "datastore_name" { + description = "The name of the datastore" + type = string + default = "my-datastore" +} + +variable "search_engine_name" { + description = "The name of the search engine" + type = string + default = "my-search-engine" +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/data_store.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/data_store.tf_updates new file mode 100644 index 00000000000..17f02ffb2d2 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/data_store.tf_updates @@ -0,0 +1,31 @@ +provider "google" { + alias = "dev_billing_override" + billing_project = var.dev_project_id + user_project_override = true +} + +resource "google_discovery_engine_data_store" "data_store_dev" { + location = var.region_vertex_ai_search + project = var.dev_project_id + data_store_id = "${var.datastore_name}" + display_name = "${var.datastore_name}" + industry_vertical = "GENERIC" + content_config = "NO_CONTENT" + solution_types = ["SOLUTION_TYPE_SEARCH"] + create_advanced_site_search = false + provider = google.dev_billing_override + depends_on = [resource.google_project_service.services] +} + +resource "google_discovery_engine_search_engine" "search_engine_dev" { + project = var.dev_project_id + engine_id = "${var.search_engine_name}" + collection_id = "default_collection" + location = google_discovery_engine_data_store.data_store_dev.location + display_name = "Search Engine App Staging" + data_store_ids = [google_discovery_engine_data_store.data_store_dev.data_store_id] + search_engine_config { + search_tier = "SEARCH_TIER_ENTERPRISE" + } + provider = google.dev_billing_override +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/vars/append__env.tfvars_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/vars/append__env.tfvars_updates new file mode 100644 index 00000000000..d663fcd0011 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/dev/vars/append__env.tfvars_updates @@ -0,0 +1,7 @@ +# Additional variables required for the Vertex AI Search pattern +search_engine_name = "sample-search-engine" +datastore_name = "sample-datastore" +vertexai_pipeline_sa_name = "vertexai-pipelines-sa" + +#The value can only be one of "global", "us" and "eu". +region_vertex_ai_search = "us" \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__cd_pipeline_triggers.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__cd_pipeline_triggers.tf_updates new file mode 100644 index 00000000000..dde1a956bb1 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__cd_pipeline_triggers.tf_updates @@ -0,0 +1,6 @@ + # ! Specific to Vertex AI Search pattern + _PIPELINE_GCS_ROOT = "gs://${resource.google_storage_bucket.data_ingestion_pipeline_gcs_root["staging"].name}" + _PIPELINE_SA_EMAIL = "${var.vertexai_pipeline_sa_name}@${var.staging_project_id}.iam.gserviceaccount.com" + _PIPELINE_CRON_SCHEDULE = var.pipeline_cron_schedule + _DATA_STORE_ID = resource.google_discovery_engine_data_store.data_store_staging.data_store_id + _REGION_VERTEX_AI_SEARCH = var.region_vertex_ai_search diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__deploy_to_prod_pipeline_triggers.tf_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__deploy_to_prod_pipeline_triggers.tf_updates new file mode 100644 index 00000000000..e5747ff7ca4 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/substitute__deploy_to_prod_pipeline_triggers.tf_updates @@ -0,0 +1,6 @@ + # ! Specific to Vertex AI Search pattern + _PIPELINE_GCS_ROOT = "gs://${resource.google_storage_bucket.data_ingestion_pipeline_gcs_root["prod"].name}" + _PIPELINE_SA_EMAIL = "${var.vertexai_pipeline_sa_name}@${var.prod_project_id}.iam.gserviceaccount.com" + _PIPELINE_CRON_SCHEDULE = var.pipeline_cron_schedule + _DATA_STORE_ID = resource.google_discovery_engine_data_store.data_store_prod.data_store_id + _REGION_VERTEX_AI_SEARCH = var.region_vertex_ai_search diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/vars/append__env.tfvars_updates b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/vars/append__env.tfvars_updates new file mode 100644 index 00000000000..3157d0d9751 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/deployment/terraform/vars/append__env.tfvars_updates @@ -0,0 +1,8 @@ +# Additional variables required for the Vertex AI Search pattern +search_engine_name = "sample-search-engine" +datastore_name = "sample-datastore" +vertexai_pipeline_sa_name = "vertexai-pipelines-sa" +pipeline_cron_schedule = "0 0 * * 0" + +#The value can only be one of "global", "us" and "eu". +region_vertex_ai_search = "us" \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/tests/integration/test_chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/tests/integration/test_chain.py new file mode 100644 index 00000000000..7460cf19fbf --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/agentic_rag_vertex_ai_search/pattern_setup/resources_to_copy/tests/integration/test_chain.py @@ -0,0 +1,77 @@ +# Copyright 2024 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. +# pylint: disable=R0801,C0415 +from unittest.mock import patch +import pytest + +from app.utils.output_types import OnToolEndEvent +from langchain_core.documents import Document +from langchain_core.messages import AIMessageChunk, HumanMessage + +CHAIN_NAME = "Agentic RAG Vertex AI Search agent" + + +@pytest.mark.asyncio +async def test_agentic_rag_vertex_ai_search_astream_events() -> None: + """ + Integration testing example for the Agentic RAG Vertex AI Search agent chain. We assert that the chain returns + events, containing AIMessageChunks and tool usage events for document retrieval. + """ + + # We mock the Vector DBs call to avoid to create this dependency during integration tests + # Patch the retrieve_docs function to return our mock document + with patch("app.chain.retrieve_docs.func") as mock_retrieve: + from app.chain import chain + + # Mock document to be returned by retrieve_docs + mock_doc = Document( + page_content="Test document content", metadata={"source": "test_source"} + ) + mock_retrieve.return_value = ("dummy content", [mock_doc]) + + user_message = HumanMessage("What is MLOps?") + input_dict = {"messages": [user_message]} + + events = [ + event async for event in chain.astream_events(input_dict, version="v2") + ] + + assert len(events) > 1, ( + f"Expected multiple events for {CHAIN_NAME} chain, " f"got {len(events)}" + ) + on_tool_end_events = [ + event for event in events if event["event"] == "on_tool_end" + ] + + assert len(on_tool_end_events) == 1, ( + f"Expected exactly one on_tool_end event for {CHAIN_NAME} chain, " + f"got {len(on_tool_end_events)}" + ) + assert isinstance( + OnToolEndEvent.model_validate(on_tool_end_events[0]), OnToolEndEvent + ) + + on_chain_stream_events = [ + event for event in events if event["event"] == "on_chat_model_stream" + ] + + assert on_chain_stream_events, ( + f"Expected at least one on_chat_model_stream event" + f" for {CHAIN_NAME} chain" + ) + + for event in on_chain_stream_events: + assert AIMessageChunk.model_validate( + event["data"]["chunk"] + ), f"Invalid AIMessageChunk for {CHAIN_NAME} chain: {event['data']['chunk']}" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/custom_rag_qa/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/custom_rag_qa/chain.py index 205980d3e2c..8bc9ef5a18b 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/custom_rag_qa/chain.py +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/custom_rag_qa/chain.py @@ -34,8 +34,9 @@ import vertexai # Configuration +LOCATION = "us-central1" EMBEDDING_MODEL = "text-embedding-004" -LLM_MODEL = "gemini-1.5-flash-002" +LLM = "gemini-1.5-flash-002" TOP_K = 5 # Initialize logging @@ -43,7 +44,7 @@ # Initialize Google Cloud and Vertex AI credentials, project_id = google.auth.default() -vertexai.init(project=project_id) +vertexai.init(project=project_id, location=LOCATION) # Set up embedding model and vector store embedding = VertexAIEmbeddings(model_name=EMBEDDING_MODEL) @@ -86,7 +87,7 @@ def should_continue() -> None: # Initialize language model -llm = ChatVertexAI(model=LLM_MODEL, temperature=0, max_tokens=1024) +llm = ChatVertexAI(model=LLM, temperature=0, max_tokens=1024) # Set up conversation inspector inspect_conversation = inspect_conversation_template | llm.bind_tools( diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/langgraph_dummy_agent/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/langgraph_dummy_agent/chain.py index e1d2c60f4b0..b6e2e3e8c42 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/langgraph_dummy_agent/chain.py +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/patterns/langgraph_dummy_agent/chain.py @@ -22,6 +22,9 @@ from langgraph.graph import END, MessagesState, StateGraph from langgraph.prebuilt import ToolNode +LOCATION = "us-central1" +LLM = "gemini-1.5-pro-002" + # 1. Define tools @tool @@ -36,7 +39,7 @@ def search(query: str) -> str: # 2. Set up the language model llm = ChatVertexAI( - model="gemini-1.5-pro-002", temperature=0, max_tokens=1024, streaming=True + model=LLM, location=LOCATION, temperature=0, max_tokens=1024, streaming=True ).bind_tools(tools) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/server.py b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/server.py index c9a8449746a..fa6591ce377 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/server.py +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/app/server.py @@ -40,6 +40,8 @@ # LangGraph dummy agent # from app.patterns.langgraph_dummy_agent.chain import chain +# Or explore other patterns in the 'app/patterns' folder + # The events that are supported by the UI Frontend SUPPORTED_EVENTS = [ "on_tool_start", diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/README.md index 5287209983e..b07288ba8d8 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/README.md +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/README.md @@ -95,6 +95,13 @@ After completing these steps, your infrastructure will be set up and ready for d For End-to-end testing of the application, including tracing and feedback sinking to BigQuery, without the need to trigger a CI/CD pipeline. +First, enable required Google Cloud APIs: + +```bash +gcloud config set project $YOUR_DEV_PROJECT +gcloud services enable serviceusage.googleapis.com cloudresourcemanager.googleapis.com +``` + After you edited the relative [`env.tfvars` file](../terraform/dev/vars/env.tfvars), follow the following instructions: ```bash diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/deploy-to-prod.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/deploy-to-prod.yaml index 93f8e734938..1abe5b5e15c 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/deploy-to-prod.yaml +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/deploy-to-prod.yaml @@ -27,7 +27,7 @@ steps: - "COMMIT_SHA=${COMMIT_SHA}" substitutions: - _PROD_PROJECT_ID: YOUR_PROD_PROJECT_ID # Replace with your prod project ID + _PROD_PROJECT_ID: YOUR_PROD_PROJECT_ID _CONTAINER_NAME: genai-app-sample _ARTIFACT_REGISTRY_REPO_NAME: genai-containers _CLOUD_RUN_APP_SA_NAME: genai-app-sample-cr-sa diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/staging.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/staging.yaml index 056dcc7f1ab..21e55e7b2d0 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/staging.yaml +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/cd/staging.yaml @@ -108,7 +108,7 @@ steps: - "--project" - "$PROJECT_ID" - "--substitutions" - - "_PROD_PROJECT_ID=${_PROD_PROJECT_ID},_ARTIFACT_REGISTRY_REPO_NAME=${_ARTIFACT_REGISTRY_REPO_NAME},_CONTAINER_NAME=${_CONTAINER_NAME},_CLOUD_RUN_APP_SA_NAME=${_CLOUD_RUN_APP_SA_NAME},_REGION=${_REGION}" + - "_CONTAINER_NAME=${_CONTAINER_NAME}" - "--sha" - $COMMIT_SHA @@ -125,7 +125,6 @@ steps: substitutions: _STAGING_PROJECT_ID: YOUR_STAGING_PROJECT_ID # Replace with your staging project ID - _PROD_PROJECT_ID: YOUR_PROD_PROJECT_ID # Replace with your prod project ID _BUCKET_NAME_LOAD_TEST_RESULTS: YOUR_CICD_PROJECT_ID-cicd-load-test-results # Replace with your GCS bucket name _CONTAINER_NAME: genai-app-sample _ARTIFACT_REGISTRY_REPO_NAME: genai-containers diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/apis.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/apis.tf index 11e3d9f1dfa..ee79e37a11a 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/apis.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/apis.tf @@ -1,33 +1,3 @@ -locals { - cicd_services = [ - "cloudbuild.googleapis.com", - "discoveryengine.googleapis.com", - "aiplatform.googleapis.com", - "serviceusage.googleapis.com", - "bigquery.googleapis.com", - "cloudresourcemanager.googleapis.com", - "cloudtrace.googleapis.com" - ] - - shared_services = [ - "aiplatform.googleapis.com", - "run.googleapis.com", - "discoveryengine.googleapis.com", - "cloudresourcemanager.googleapis.com", - "iam.googleapis.com", - "bigquery.googleapis.com", - "serviceusage.googleapis.com", - "logging.googleapis.com", - "cloudtrace.googleapis.com" - ] - - projects = { - prod = var.prod_project_id - staging = var.staging_project_id - } - -} - resource "google_project_service" "cicd_services" { count = length(local.cicd_services) project = var.cicd_runner_project_id @@ -37,9 +7,9 @@ resource "google_project_service" "cicd_services" { resource "google_project_service" "shared_services" { for_each = { - for pair in setproduct(keys(local.projects), local.shared_services) : + for pair in setproduct(keys(local.deploy_project_ids), local.shared_services) : "${pair[0]}_${replace(pair[1], ".", "_")}" => { - project = local.projects[pair[0]] + project = local.deploy_project_ids[pair[0]] service = pair[1] } } diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/build_triggers.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/build_triggers.tf index 17556f7ec5c..87ca8673fa9 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/build_triggers.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/build_triggers.tf @@ -16,6 +16,7 @@ resource "google_cloudbuild_trigger" "pr_checks" { filename = "deployment/ci/pr_checks.yaml" included_files = [ "app/**", + "data_processing/**", "tests/**", "deployment/**", "poetry.lock" @@ -41,17 +42,18 @@ resource "google_cloudbuild_trigger" "cd_pipeline" { filename = "deployment/cd/staging.yaml" included_files = [ "app/**", + "data_processing/**", "tests/**", "deployment/**", "poetry.lock" ] substitutions = { _STAGING_PROJECT_ID = var.staging_project_id - _PROD_PROJECT_ID = var.prod_project_id _BUCKET_NAME_LOAD_TEST_RESULTS = resource.google_storage_bucket.bucket_load_test_results.name _ARTIFACT_REGISTRY_REPO_NAME = var.artifact_registry_repo_name _CLOUD_RUN_APP_SA_NAME = var.cloud_run_app_sa_name _REGION = var.region + # Your other CD Pipeline substitutions } depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] @@ -71,6 +73,14 @@ resource "google_cloudbuild_trigger" "deploy_to_prod_pipeline" { approval_config { approval_required = true } + substitutions = { + _PROD_PROJECT_ID = var.prod_project_id + _ARTIFACT_REGISTRY_REPO_NAME = var.artifact_registry_repo_name + _CLOUD_RUN_APP_SA_NAME = var.cloud_run_app_sa_name + _REGION = var.region + + # Your other Deploy to Prod Pipeline substitutions + } depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] } diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/iam.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/iam.tf index 1b68c02eb0c..1c2a6fc2033 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/iam.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/iam.tf @@ -32,4 +32,3 @@ resource "google_project_iam_member" "default_compute_sa_storage_object_creator" member = "serviceAccount:${data.google_project.dev_project.number}-compute@developer.gserviceaccount.com" depends_on = [resource.google_project_service.services] } - diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/variables.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/variables.tf index eddb5d9f607..874b3226df9 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/variables.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/dev/variables.tf @@ -61,4 +61,4 @@ variable "cloud_run_app_roles" { "roles/cloudtrace.agent", "roles/storage.admin" ] -} \ No newline at end of file +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/iam.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/iam.tf index ddd3587294f..dd675d89018 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/iam.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/iam.tf @@ -1,13 +1,6 @@ -locals { - project_ids = { - prod = var.prod_project_id - staging = var.staging_project_id - } -} - # Data source to get project numbers data "google_project" "projects" { - for_each = local.project_ids + for_each = local.deploy_project_ids project_id = each.value } @@ -25,9 +18,9 @@ resource "google_project_iam_member" "cicd_project_roles" { # 2. Assign roles for the other two projects (prod and staging) resource "google_project_iam_member" "other_projects_roles" { for_each = { - for pair in setproduct(keys(local.project_ids), var.cicd_sa_deployment_required_roles) : + for pair in setproduct(keys(local.deploy_project_ids), var.cicd_sa_deployment_required_roles) : "${pair[0]}-${pair[1]}" => { - project_id = local.project_ids[pair[0]] + project_id = local.deploy_project_ids[pair[0]] role = pair[1] } } @@ -40,7 +33,7 @@ resource "google_project_iam_member" "other_projects_roles" { # 3. Allow Cloud Run service SA to pull containers stored in the CICD project resource "google_project_iam_member" "cicd_run_invoker_artifact_registry_reader" { - for_each = local.project_ids + for_each = local.deploy_project_ids project = var.cicd_runner_project_id role = "roles/artifactregistry.reader" @@ -52,9 +45,9 @@ resource "google_project_iam_member" "cicd_run_invoker_artifact_registry_reader" # 4. Grant Cloud Run SA the required permissions to run the application resource "google_project_iam_member" "cloud_run_app_sa_roles" { for_each = { - for pair in setproduct(keys(local.project_ids), var.cloud_run_app_roles) : + for pair in setproduct(keys(local.deploy_project_ids), var.cloud_run_app_roles) : join(",", pair) => { - project = local.project_ids[pair[0]] + project = local.deploy_project_ids[pair[0]] role = pair[1] } } diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/locals.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/locals.tf new file mode 100644 index 00000000000..7abea6c5334 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/locals.tf @@ -0,0 +1,36 @@ +locals { + cicd_services = [ + "cloudbuild.googleapis.com", + "discoveryengine.googleapis.com", + "aiplatform.googleapis.com", + "serviceusage.googleapis.com", + "bigquery.googleapis.com", + "cloudresourcemanager.googleapis.com", + "cloudtrace.googleapis.com" + ] + + shared_services = [ + "aiplatform.googleapis.com", + "run.googleapis.com", + "discoveryengine.googleapis.com", + "cloudresourcemanager.googleapis.com", + "iam.googleapis.com", + "bigquery.googleapis.com", + "serviceusage.googleapis.com", + "logging.googleapis.com", + "cloudtrace.googleapis.com" + ] + + deploy_project_ids = { + prod = var.prod_project_id + staging = var.staging_project_id + } + + all_project_ids = [ + var.cicd_runner_project_id, + var.prod_project_id, + var.staging_project_id + ] + +} + diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/log_sinks.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/log_sinks.tf index 8119a242f19..58c6065a288 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/log_sinks.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/log_sinks.tf @@ -1,5 +1,5 @@ resource "google_project_iam_member" "bigquery_data_editor" { - for_each = local.project_ids + for_each = local.deploy_project_ids project = each.value role = "roles/bigquery.dataEditor" @@ -7,7 +7,7 @@ resource "google_project_iam_member" "bigquery_data_editor" { } module "log_export_to_bigquery" { - for_each = local.project_ids + for_each = local.deploy_project_ids source = "terraform-google-modules/log-export/google" version = "10.0.0" @@ -24,7 +24,7 @@ module "log_export_to_bigquery" { } resource "google_bigquery_dataset" "feedback_dataset" { - for_each = local.project_ids + for_each = local.deploy_project_ids project = each.value dataset_id = var.feedback_bigquery_dataset_id friendly_name = var.feedback_bigquery_dataset_id @@ -34,7 +34,7 @@ resource "google_bigquery_dataset" "feedback_dataset" { } module "feedback_export_to_bigquery" { - for_each = local.project_ids + for_each = local.deploy_project_ids source = "terraform-google-modules/log-export/google" version = "10.0.0" @@ -50,7 +50,7 @@ module "feedback_export_to_bigquery" { resource "google_bigquery_dataset" "telemetry_logs_dataset" { depends_on = [module.log_export_to_bigquery, module.feedback_export_to_bigquery, resource.google_project_service.shared_services] - for_each = local.project_ids + for_each = local.deploy_project_ids project = each.value dataset_id = var.telemetry_bigquery_dataset_id friendly_name = var.telemetry_bigquery_dataset_id diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/service_accounts.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/service_accounts.tf index d158071bab8..ba5d2e85c1b 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/service_accounts.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/service_accounts.tf @@ -6,7 +6,7 @@ resource "google_service_account" "cicd_runner_sa" { } resource "google_service_account" "cloud_run_app_sa" { - for_each = local.project_ids + for_each = local.deploy_project_ids account_id = var.cloud_run_app_sa_name display_name = "Cloud Run Generative AI app SA" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/storage.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/storage.tf index 56fd65d9ffd..4a964ab564c 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/storage.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/storage.tf @@ -8,14 +8,6 @@ terraform { } } -locals { - all_projects = [ - var.cicd_runner_project_id, - var.prod_project_id, - var.staging_project_id - ] -} - resource "google_storage_bucket" "bucket_load_test_results" { name = "${var.cicd_runner_project_id}-${var.suffix_bucket_name_load_test_results}" location = var.region @@ -26,7 +18,7 @@ resource "google_storage_bucket" "bucket_load_test_results" { } resource "google_storage_bucket" "logs_data_bucket" { - for_each = toset(local.all_projects) + for_each = toset(local.all_project_ids) name = "${each.value}-logs-data" location = var.region project = each.value @@ -35,3 +27,4 @@ resource "google_storage_bucket" "logs_data_bucket" { depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] } + diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/variables.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/variables.tf index 57e7f05f636..719be55d040 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/variables.tf +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/deployment/terraform/variables.tf @@ -121,5 +121,10 @@ variable "cicd_roles" { variable "cicd_sa_deployment_required_roles" { description = "List of roles to assign to the CICD runner service account for the Staging and Prod projects." type = list(string) - default = ["roles/run.developer", "roles/iam.serviceAccountUser"] + default = [ + "roles/run.developer", + "roles/iam.serviceAccountUser", + "roles/aiplatform.user", + "roles/storage.admin" + ] } diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/poetry.lock b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/poetry.lock index e9aa44011fb..cbce8ad05ef 100644 --- a/gemini/sample-apps/e2e-gen-ai-app-starter-pack/poetry.lock +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-pack/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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