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a/gemini/sample-apps/conversational-genai-app-template/.gitignore b/gemini/sample-apps/conversational-genai-app-template/.gitignore new file mode 100644 index 00000000000..7716af9b34d --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/.gitignore @@ -0,0 +1,198 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*.pyc +*$py.class +**/dist +/tmp +/out-tsc +/bazel-out + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +Pipfile.lock +Pipfile + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +.venv* +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# macOS +.DS_Store + +# PyCharm +.idea + +# User-specific files +language/examples/prompt-design/train.csv +README-TOC*.md + +.terraform* +.Terraform* +terraform.tfstate +terraform.tfstate.backup + +tmp* + +# Node +**/node_modules +npm-debug.log +yarn-error.log + +# IDEs and editors +.idea/ +.project +.classpath +.c9/ +*.launch +.settings/ +*.sublime-workspace + +# Visual Studio Code +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +.history/* + +# Miscellaneous +**/.angular/* +/.angular/cache +.sass-cache/ +/connect.lock +/coverage +/libpeerconnection.log +testem.log +/typings + +# System files +.DS_Store +Thumbs.db +*.vscode* + +.persist_vector_store +tests/load_test/.results/*.html +tests/load_test/.results/*.csv +locust_env +my_env.tfvars +.streamlit_chats +.saved_chats diff --git a/gemini/sample-apps/conversational-genai-app-template/CONTRIBUTING.md b/gemini/sample-apps/conversational-genai-app-template/CONTRIBUTING.md new file mode 100644 index 00000000000..625bb145a95 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/CONTRIBUTING.md @@ -0,0 +1,46 @@ +# How to Contribute + +We'd love to accept your patches and contributions to this sample. There are +just a few small guidelines you need to follow. + +## Contributor License Agreement +Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one. + +You generally only need to submit a CLA once, so if you've already submitted one (even if it was for a different project), you probably don't need to do it again. + +## Community Guidelines, Code Reviews, Contributor Guide +Please refer to the [root repository CONTRIBUTING.md file](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/CONTRIBUTING.md) for Community Guidelines, Code Reviews, Contributor Guide, or specific guidance for Google Employees. + +## Code Quality Checks + +To ensure code quality, we utilize automated checks. Before submitting a pull request, please run the following commands locally: + +```bash +poetry install --with streamlit,jupyter,lint +``` + +This installs development dependencies, including linting tools. + +Then, execute the following Makefile targets: + +```bash +make lint +``` + +This command runs the following linters to check for code style, potential errors, and type hints: +- **codespell**: Detects common spelling mistakes in code and documentation. +- **ruff**: A fast linter that combines the functionality of several popular tools like flake8, isort, pycodestyle, and others. +- **mypy**: Performs static type checking to catch type errors before runtime. + +```bash +make test +``` + +This command runs the test suite using pytest, covering both unit and integration tests: +- **`poetry run pytest tests/unit`**: Executes unit tests located in the `tests/unit` directory. +- **`poetry run pytest tests/integration`**: Executes integration tests located in the `tests/integration` directory. + + +Your pull request will also be automatically checked by these tools using GitHub Actions. Ensuring your code passes these checks locally will help expedite the review process. + + diff --git a/gemini/sample-apps/conversational-genai-app-template/Dockerfile b/gemini/sample-apps/conversational-genai-app-template/Dockerfile new file mode 100644 index 00000000000..b87eb98a03f --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.11-slim + +RUN pip install poetry==1.6.1 + +RUN poetry config virtualenvs.create false + +WORKDIR /code + +COPY ./pyproject.toml ./README.md ./poetry.lock* ./ + +COPY ./app ./app + +RUN poetry install --no-interaction --no-ansi --no-dev + +EXPOSE 8080 + +CMD exec uvicorn app.server:app --host 0.0.0.0 --port 8080 diff --git a/gemini/sample-apps/conversational-genai-app-template/Makefile b/gemini/sample-apps/conversational-genai-app-template/Makefile new file mode 100644 index 00000000000..d5642962d04 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/Makefile @@ -0,0 +1,19 @@ +test: + poetry run pytest tests/unit && poetry run pytest tests/integration + +playground: + poetry run uvicorn app.server:app --host 0.0.0.0 --port 8000 --reload & poetry run streamlit run streamlit/streamlit_app.py --browser.serverAddress=localhost --server.enableCORS=false --server.enableXsrfProtection=false + +backend: + poetry run uvicorn app.server:app --host 0.0.0.0 --port 8000 --reload + +frontend: + poetry run streamlit run streamlit/streamlit_app.py --browser.serverAddress=localhost --server.enableCORS=false --server.enableXsrfProtection=false + +load_test: + poetry run locust -f tests/load_test/load_test.py -H $RUN_SERVICE_URL --headless -t 30s -u 60 -r 2 --csv=tests/load_test/.results/results --html=tests/load_test/.results/report.html + +lint: + poetry run codespell + poetry run ruff . + poetry run mypy app tests diff --git a/gemini/sample-apps/conversational-genai-app-template/README.md b/gemini/sample-apps/conversational-genai-app-template/README.md new file mode 100644 index 00000000000..d9a653697c6 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/README.md @@ -0,0 +1,189 @@ +# πŸš€ Conversational GenAI App Template! πŸš€ +>**Focus on Innovation, not Infrastructure** + +This folder is a **starter-pack** to building a Generative AI application on Google Cloud Platform (GCP). + +It is meant to be a template repository to build your own GenAI application. + +We provide a comprehensive set of resources to guide you through the entire development process, from prototype to production. + +## High-Level Architecture + +This template covers all aspects of GenAI app development, from prototyping and evaluation to deployment and monitoring. + +![High Level Architecture](images/high_level_architecture.png "Architecture") + +## What's in this Starter Pack? + +
+A prod-ready FastAPI server + +| Description | Visualization | +|------------------------------------------------------------------------------------------------------------------------------------------------------|---------------| +| The template includes a production-ready FastAPI server with real-time chat interface, event streaming, auto-generated docs. Designed for scalability and easy integration with monitoring tools. | ![FastAPI docs](images/fastapi_docs.png) +![FastAPI docs](images/code.png) | + +
+ +
+Ready-to-use AI patterns + +| Description | Visualization | +|------------------------------------------------------------------------------------------------------------------------------------------------------|---------------| +| Start with a variety of common patterns: this repository offers examples including a basic conversational chain, a production-ready RAG (Retrieval-Augmented Generation) chain developed with python, and a LangGraph agent implementation. Use them in the application by changing one line of code. See the [README](app/README.md) for more details. | ![patterns available](images/patterns_available.png) | + +
+ +
+Integration with Vertex AI Evaluation and Experiments + +| Description | Visualization | +|-------------|---------------| +| The repository showcases how to evaluate GenAI applications using tools like Vertex AI rapid eval SDK and Vertex AI Experiments. | ![Vertex AI Rapid Eval](images/vertex_ai_rapid_eval.png) | + +
+ + +
+Leverage GCP Native Tracing and Logging + +| Description | Visualization | +|-------------|---------------| +| Seamlessly integrate with OpenTelemetry, Cloud Trace, Cloud Logging and BigQuery for comprehensive data collection. | ![Tracing Preview](images/tracing_preview.png) | + +
+ + +
+ +Monitor Responses from the application + +| Description | Visualization | +|-------------|---------------| +| Monitor your GenAI Application's performance. We provide a Looker Studio [dashboard](https://lookerstudio.google.com/u/0/reporting/fa742264-4b4b-4c56-81e6-a667dd0f853f) to monitor application conversation statistics and user feedback. | ![Dashboard1](images/dashboard_1.png) | +| We can also drill down to individual conversations and view the messages exchanged | ![Dashboard2](images/dashboard_2.png) | + +
+ +
+CICD and Terraform + +| Description | Visualization | +|-------------|---------------| +| Streamline your deployments with Cloud Build. Enhance reliability through automated testing. The starter pack includes implementation of unit, integration and load tests and a set of terraform resources for you to setup your own GCP project in a matter of minutes. | ![cicd](images/cicd.png) | + +
+ +
+A comprehensive UI Playground + +| Description | Visualization | +|-------------|---------------| +| Experiment with your GenAI Application in a feature-rich playground, including chat curation, user feedback collection, multimodal input, and more! | ![Streamlit View](images/streamlit_view.png) | + +
+ + +## Getting Started + +### Prerequisites + +- Python 3.10+ +- Google Cloud SDK installed and configured. +- [Poetry](https://python-poetry.org/docs/#installation) for dependency management + +### Download the starter pack + +```bash +gsutil -m cp -r gs://genai-starter-pack-templates/genai-starter-pack-template . +``` +Use the downloaded folder as a starting point for your own GenAI application. + + +### Installation + +Install required packages using Poetry: + +```bash +poetry install --with streamlit,jupyter +``` + +### Setup +Set your default GCP project and region: + +```commandline +export PROJECT_ID="YOUR_PROJECT_ID" +export REGION="YOUR_REGION" +gcloud config set project $PROJECT_ID +gcloud config set region $REGION +``` + +## Commands + +| Command | Description | +|---------|-------------| +| `make playground` | Start the backend and frontend for local playground execution | +| `make test` | Run unit and integration tests | +| `make load_test` | Execute load tests (see [tests/load_test/README.md](tests/load_test/README.md) for details) | +| `poetry run jupyter` | Launch Jupyter notebook | + +For full command options and usage, refer to the [Makefile](Makefile). + +## Usage + +1. **Prototype Your Chain:** Build your GenAI Application using different methodologies and frameworks. Use Vertex AI Evaluation for assessing the performances of your application and relative chain of steps. **See [`notebooks/getting_started.ipynb`](notebooks/getting_started.ipynb) for a tutorial to get started building and evaluating your chain.** +2. **Integrate into the App:** Import your chain into the app. Edit `app/chain.py` file to add your chain. +3. **Playground Testing:** Explore your chain's functionality using the Streamlit playground. Take advantage of the comprehensive playground features, such as chat history management, user feedback mechanisms, support for various input types, and additional capabilities. You can run the playground locally with `make playground` command. +4. **Deploy with CI/CD:** Configure and trigger the CI/CD pipelines. Edit tests if needed. See the [deployment section](#deployment) below for more details. +5. **Monitor in Production:** Track performance and gather insights using Cloud Logging, Tracing, and the Looker Studio dashboard. Use the gathered data to iterate on your GenAI application. + + +## Deployment + +**Dev Environment** + +You can test deployment towards a Dev Environment using the following command: +```bash +gcloud run deploy conversational-app-sample --source . --project YOUR_DEV_PROJECT_ID +``` + +The repository includes a Terraform configuration for setup of the Dev GCP project. +See [deployment/README.md](deployment/README.md) for instructions. + + +**Production Deployment with Terraform:** + +![Deployment Workflow](images/deployment_workflow.png) + +**Quick Start:** +1. Enable required APIs in the CI/CD project. + ```bash + gcloud config set project YOUR_CI_CD_PROJECT_ID + gcloud services enable serviceusage.googleapis.com cloudresourcemanager.googleapis.com cloudbuild.googleapis.com secretmanager.googleapis.com + ``` +2. Create a Git repository (GitHub, GitLab, Bitbucket). +3. Connect to Cloud Build following [Cloud Build Repository Setup](https://cloud.google.com/build/docs/repositories#whats_next). +4. Configure [`deployment/terraform/vars/env.tfvars`](deployment/terraform/vars/env.tfvars) with your project details. +5. Deploy infrastructure: + +```bash +cd deployment/terraform +terraform init +terraform apply --var-file vars/env.tfvars +``` +6. Perform a commit and push to the repository to see the CI/CD pipelines in action! + +For detailed deployment instructions, refer to [deployment/README.md](deployment/README.md). + + +## Contributing + +Contributions are welcome! See the [Contributing Guide](CONTRIBUTING.md). + +## Getting Help + +Please use the [issues page](https://github.com/your-repo/genai-starter-pack/issues) for suggestions, feedback, or bug reports. + +## Disclaimer + +This repository is for demonstrative purposes only and is not an officially supported Google product. \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/README.md b/gemini/sample-apps/conversational-genai-app-template/app/README.md new file mode 100644 index 00000000000..c006a85f36e --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/README.md @@ -0,0 +1,75 @@ +# Sample Chatbot Application + +This folder implements a chatbot application using FastAPI, and Google Cloud services. It supports multiple conversation patterns and can be easily extended with new chains. + +## Folder Structure + +``` +. +β”œβ”€β”€ server.py # Main FastAPI server +β”œβ”€β”€ chain.py # Default chain implementation +β”œβ”€β”€ patterns/ # Conversation pattern implementations +β”‚ β”œβ”€β”€ custom_rag_qa/ +β”‚ └── langgraph_dummy_agent/ +β”œβ”€β”€ utils/ # Utility functions and classes +└── eval/ # Evaluation tools and data +``` +## GenAI Application Patterns + +### 1. Default Chain + +The default chain is a simple conversational bot that produces recipes based on user questions. It uses the Gemini 1.5 Flash model. + +### 2. Custom RAG QA + +A pythonic RAG (Retrieval-Augmented Generation) chain designed for maximum flexibility in orchestrating different components. It includes: + +- Query rewriting +- Document retrieval and ranking +- LLM-based response generation + +### 3. Langgraph Dummy Agent + +A simple agent implemented using Langgraph, a framework for building agent and multi-agent workflows. + +### Switching Between Patterns + +To switch between different patterns, modify the import statement in `server.py`. + +All chains have the same interface, allowing for seamless swapping without changes to the Streamlit frontend. + + +## Monitoring and Observability + +![monitoring_flow](../images/monitoring_flow.png) + +### Trace and Log Capture + +This application utilizes [OpenTelemetry](https://opentelemetry.io/) and [OpenLLMetry](https://github.com/traceloop/openllmetry) for comprehensive observability, emitting events to Google Cloud Trace and Google Cloud Logging. Every interaction with Langchain and VertexAI is instrumented (see [`server.py`](server.py)), enabling detailed tracing of request flows throughout the application. + +Leveraging the [CloudTraceSpanExporter](https://cloud.google.com/python/docs/reference/spanner/latest/opentelemetry-tracing), the application captures and exports tracing data. To address the limitations of Cloud Trace ([256-byte attribute value limit](https://cloud.google.com/trace/docs/quotas#limits_on_spans)) and [Cloud Logging](https://cloud.google.com/logging/quotas) ([256KB log entry size](https://cloud.google.com/logging/quotas)), a custom extension of the CloudTraceSpanExporter is implemented in [`app/utils/tracing.py`](app/utils/tracing.py). + +This extension enhances observability by: + +* **Creating a corresponding Google Cloud Logging entry for every captured event.** +* **Automatically storing event data in Google Cloud Storage when the payload exceeds 256KB.** + +Logged payloads are associated with the original trace, ensuring seamless access from the Cloud Trace console. + +### Log Router + +Events are forwarded to BigQuery through a [log router](https://cloud.google.com/logging/docs/routing/overview) for long-term storage and analysis. The deployment of the log router is done via Terraform code in [deployment/terraform](../deployment/terraform). + +### Looker Studio Dashboard + +Once the data is written to BigQuery, it can be used to populate a [Looker Studio dashboard](TODO: Add link). + +This dashboard, offered as a template, provides a starting point for building custom visualizations on the top of the data being captured. + + + + + + + + diff --git a/gemini/sample-apps/conversational-genai-app-template/app/__init__.py b/gemini/sample-apps/conversational-genai-app-template/app/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gemini/sample-apps/conversational-genai-app-template/app/chain.py b/gemini/sample-apps/conversational-genai-app-template/app/chain.py new file mode 100644 index 00000000000..c126d460413 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/chain.py @@ -0,0 +1,40 @@ +# 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 ChatPromptTemplate, MessagesPlaceholder +from langchain_google_vertexai import ChatVertexAI, HarmBlockThreshold, HarmCategory + +safety_settings = { + HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_ONLY_HIGH, +} + +llm = ChatVertexAI( + model_name="gemini-1.5-flash-001", temperature=0, max_output_tokens=1024, + safety_settings=safety_settings +) + + +template = ChatPromptTemplate.from_messages( + [ + ("system", """You are a conversational bot that produce recipes for users based + on a question."""), + MessagesPlaceholder(variable_name="messages") + ] +) + +chain = template | llm diff --git a/gemini/sample-apps/conversational-genai-app-template/app/eval/data/chats.yaml b/gemini/sample-apps/conversational-genai-app-template/app/eval/data/chats.yaml new file mode 100644 index 00000000000..bb950ac2bf1 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/eval/data/chats.yaml @@ -0,0 +1,42 @@ +- messages: + - type: human + content: Hi + - type: ai + content: Hi, how can I help you? + - type: human + content: I'm looking for a recipe for a healthy dinner. Do you have any recommendations? + - type: ai + content: Sure, I can help you with that. What are your dietary restrictions? Are you vegetarian, vegan, gluten-free, or anything else? + - type: human + content: I'm not vegetarian or vegan, but I am gluten-free. + - type: ai + content: 'Okay, I ll keep that in mind. Here are a few recipes that I think you might like: * **Grilled Salmon with Roasted Vegetables:** This is a delicious and healthy recipe that is perfect for a weeknight meal. The salmon is grilled to perfection and the roasted vegetables add a touch of sweetness. * **Chicken Stir-Fry:** This is a quick and easy stir-fry that is perfect for busy weeknights. The chicken is cooked with vegetables and a light sauce. * **Lentil Soup:** This is a hearty and healthy soup that is perfect for a cold winter day. The lentils are packed with protein and fiber, and the soup is also gluten-free.' + - type: human + content: Those all sound great! I think I'm going to try the grilled salmon with roasted vegetables. + - type: ai + content: That's a great choice! I hope you enjoy it. + - type: human + content: Thanks for your help! + - type: ai + content: You're welcome! Is there anything else I can help you with today? + - type: human + content: No, that's all. Thanks again! + - type: ai + content: You're welcome! Have a great day! +- messages: + - type: human + content: Hi + - type: ai + content: Hi, how can I help you? + - type: human + content: I'm looking for a recipe for a romantic dinner. Do you have any recommendations? + - type: ai + content: Sure, I can help you with that. What are your dietary restrictions? Are you vegetarian, vegan, gluten-free, or anything else? + - type: human + content: I'm vegetarian. + - type: ai + content: 'Sure, I can help you find a healthy vegetarian dinner recipe. Here are a few ideas: * **Burnt aubergine veggie chilli:** This is a hearty and flavorful dish that is packed with nutrients. The roasted aubergine gives it a smoky flavor, and the lentils and beans add protein and fiber. * **Simple mushroom curry:** This is a quick and easy curry that is perfect for a weeknight meal. The mushrooms are cooked in a creamy sauce with spices, and the whole dish is ready in under 30 minutes. * **Vegetarian enchiladas:** This is a classic Mexican dish that is easy to make vegetarian. The enchiladas are filled with a variety of vegetables, and they are topped with a delicious sauce. * **Braised sesame tofu:** This is a flavorful and satisfying dish that is perfect for a cold night. The tofu is braised in a sauce with sesame, ginger, and garlic, and it is served over rice or noodles. * **Roast garlic & tahini spinach:** This is a light and healthy dish that is perfect for a spring or summer meal. The spinach is roasted with garlic and tahini, and it is served with a side of pita bread. These are just a few ideas to get you started. There are many other great vegetarian dinner recipes out there, so you are sure to find something that you will enjoy.' + - type: human + content: Those all sound great! I like the Burnt aubergine veggie chilli + - type: ai + content: That's a great choice! I hope you enjoy it. \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/eval/utils.py b/gemini/sample-apps/conversational-genai-app-template/app/eval/utils.py new file mode 100644 index 00000000000..9892381ef63 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/eval/utils.py @@ -0,0 +1,239 @@ +# 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 glob +import logging +import os +from concurrent.futures import ThreadPoolExecutor +from functools import partial +from typing import Any, Callable, Dict, Iterator, List + +import nest_asyncio +import pandas as pd +import yaml +from tqdm import tqdm + +nest_asyncio.apply() + +def load_chats(path: str) -> List[Dict[str, Any]]: + """ + Loads a list of chats from a directory or file. + + Args: + path (str): The path to the directory or file containing the chats. + + Returns: + List[Dict[str, Any]]: A list of chats. + """ + + chats: List[Dict[str, Any]] = [] + for path in glob.glob(path): + with open(path) as f: + chats_in_file = yaml.safe_load(f) + chats = chats + chats_in_file + return chats + +def pairwise(iterable: List[Any]) -> Iterator[tuple[Any, Any]]: + """Creates an iterable with tuples paired together + e.g s -> (s0, s1), (s2, s3), (s4, s5), ... + """ + a = iter(iterable) + return zip(a, a) + + +def generate_multiturn_history(df: pd.DataFrame) -> pd.DataFrame: + """Processes a DataFrame of conversations to create a multi-turn history. + + This function iterates through a DataFrame where each row represents a conversation. + It extracts human and AI messages from the "messages" column and structures them + into a new DataFrame. Each row in the output DataFrame represents a single turn + in a conversation, including the human message, AI message, and the conversation + history up to that point. + + Args: + df (pd.DataFrame): A DataFrame where each row represents a conversation. + The DataFrame should have a column named "messages" containing + a list of alternating human and AI messages. + + Returns: + pd.DataFrame: A DataFrame where each row represents a single turn in a conversation. + The DataFrame has the following columns: + - human_message: The human message in that turn. + - ai_message: The AI message in that turn. + - conversation_history: A list of all messages in the conversation + up to and including the current turn. + """ + processed_messages = [] + for _, row in df.iterrows(): + conversation_history: List[str] = [] + for human_message, ai_message in pairwise(row["messages"]): + message = { + "human_message": human_message, + "ai_message": ai_message, + "conversation_history": conversation_history + } + conversation_history = conversation_history + [ + human_message, ai_message + ] + processed_messages.append(message) + + return pd.DataFrame(processed_messages) + + +def generate_message(row: tuple[int, Dict[str, Any]], callable: Any) -> Dict[str, Any]: + """Generates a response message using a given callable and updates the row dictionary. + + This function takes a row dictionary containing message data and a callable object. + It extracts conversation history and the current human message from the row, + then uses the callable to generate a response based on the conversation history. + The generated response content and usage metadata are then added to the original + message dictionary within the row. + + Args: + row (tuple[int, Dict[str, Any]]): A tuple containing the index and a dictionary + with message data, including: + - "conversation_history" (List[str]): Optional. List of previous + messages + in the conversation. + - "human_message" (str): The current human message. + callable (Any): A callable object that takes a dictionary with a "messages" key + and returns a response object with "content" and + "usage_metadata" attributes. + + Returns: + Dict[str, Any]: The updated row dictionary with the generated response added to the message. + The message will now contain: + - "response" (str): The generated response content. + - "response_obj" (Any): The usage metadata of the response from the callable. + """ + index, message = row + messages = message["conversation_history"] if "conversation_history" in message else [] + messages.append(message["human_message"]) + input_callable = {"messages": messages} + response = callable.invoke(input_callable) + message["response"] = response.content + message["response_obj"] = response.usage_metadata + return message + + +def batch_generate_messages( + messages: pd.DataFrame, + callable: Callable[[List[Dict[str, Any]]], Dict[str, Any]], + max_workers: int = 4 +) -> pd.DataFrame: + """Generates AI responses to user messages using a provided callable. + + Processes a Pandas DataFrame containing conversation histories and user messages, utilizing + the specified callable to predict AI responses in parallel. + + Args: + messages (pd.DataFrame): DataFrame with a 'messages' column. Each row + represents a conversation and contains a list of dictionaries, where + each dictionary + represents a message turn in the format: + + ```json + [ + {"role": "human", "content": "user's message"}, + {"role": "ai", "content": "AI's response"}, + {"role": "human", "content": "current user's message"}, + ... + ] + ``` + + callable (Callable[[List[Dict[str, Any]]], Dict[str, Any]]): Callable object + (e.g., Langchain Chain) used + for response generation. It should accept a list of message dictionaries + (as described above) and return a dictionary with the following structure: + + ```json + { + "response": "AI's response", + "response_obj": { ... } # optional response metadata + } + ``` + + max_workers (int, optional): Number of worker processes for parallel + prediction. Defaults to 4. + + Returns: + pd.DataFrame: DataFrame with the original 'messages' column and two new + columns: 'response' containing the predicted AI responses, and + 'response_obj' containing optional response metadata. + + Example: + ```python + import pandas as pd + + messages_df = pd.DataFrame({ + "messages": [ + [ + {"role": "human", "content": "What's the weather today?"} + ], + [ + {"role": "human", "content": "Tell me a joke."}, + {"role": "ai", "content": "Why did the scarecrow win an award?"}, + {"role": "human", "content": "I don't know, why?"} + ] + ] + }) + + responses_df = batch_generate_messages(my_callable, messages_df) + ``` + """ + logging.info("Executing batch scoring") + predicted_messages = [] + with ThreadPoolExecutor(max_workers) as pool: + partial_func = partial(generate_message, callable=callable) + for message in tqdm( + pool.map(partial_func, messages.iterrows()), total=len(messages) + ): + predicted_messages.append(message) + return pd.DataFrame(predicted_messages) + +def save_df_to_csv(df: pd.DataFrame, dir_path: str, filename: str) -> None: + """Saves a pandas DataFrame to directory as a CSV file. + + Args: + df: The DataFrame to save. + filename: The desired filename. Should end in `.csv`. + dir_path: The desired dir_path. + """ + + filepath = os.path.join(dir_path, filename) + os.makedirs(dir_path, exist_ok=True) + df.to_csv(filepath, index=False) + logging.info(f"DataFrame saved to: {filepath}") + + +def import_llm_chain(llm_chain_path: str) -> Any: + """Dynamically imports the specified LLM chain class.""" + llm_chain_module, llm_chain_name = llm_chain_path.rsplit(".", 1) + module = __import__(llm_chain_module, fromlist=[llm_chain_name]) + llm_chain = getattr(module, llm_chain_name) + return llm_chain + + +def prepare_metrics(metrics: List[str]) -> List[Any]: + """Prepares the list of metrics for evaluation, handling custom metrics.""" + metrics_evaluation = [] + for metric in metrics: + if metric.startswith("custom:"): + *module_path, metric_name = metric.removeprefix("custom:").split(".") + metrics_evaluation.append( + __import__(".".join(module_path), fromlist=[metric_name]).__dict__[ + metric_name]) + else: + metrics_evaluation.append(metric) + return metrics_evaluation diff --git a/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/chain.py b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/chain.py new file mode 100644 index 00000000000..7ef2475287e --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/chain.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. + +# mypy: disable-error-code="arg-type" +import logging +from typing import Any, Dict, Iterator + +import google +import vertexai +from langchain_google_community.vertex_rank import VertexAIRank +from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings + +from app.patterns.custom_rag_qa.templates import query_rewrite_template, rag_template +from app.patterns.custom_rag_qa.vector_store import get_vector_store +from app.utils.output_types import OnChatModelStreamEvent, OnToolEndEvent, custom_chain + +# Configuration +EMBEDDING_MODEL = "text-embedding-004" +LLM_MODEL = "gemini-1.5-flash-001" +TOP_K = 5 + +# Initialize logging +logging.basicConfig(level=logging.INFO) + +credentials, project_id = google.auth.default() +vertexai.init(project=project_id) +embedding = VertexAIEmbeddings(model_name=EMBEDDING_MODEL) +vector_store = get_vector_store(embedding=embedding) +retriever = vector_store.as_retriever(search_kwargs={"k": 20}) +reranker = VertexAIRank( + project_id=project_id, + location_id="global", + ranking_config="default_ranking_config", + title_field="id", + top_n=TOP_K, +) +llm = ChatVertexAI(model=LLM_MODEL, temperature=0, max_tokens=1024) + +query_gen = query_rewrite_template | llm +response_chain = rag_template | llm + + +@custom_chain +def chain(input: Dict[str, Any], **kwargs: Any) -> Iterator[OnToolEndEvent | OnChatModelStreamEvent]: + """ + Implements a RAG QA chain. Decorated with `custom_chain` to offer Langchain compatible astream_events + and invoke interface and OpenTelemetry tracing. + """ + # Generate optimized query + query = query_gen.invoke(input).content + + # Retrieve and rank documents + retrieved_docs = retriever.invoke(query) + ranked_docs = reranker.compress_documents(documents=retrieved_docs, query=query) + + # Yield tool results metadata + yield OnToolEndEvent(data={"input": {"query": query}, "output": ranked_docs}) + + # Stream LLM response + for chunk in response_chain.stream( + input={"messages": input["messages"], "relevant_documents": ranked_docs} + ): + yield OnChatModelStreamEvent(data={"chunk": chunk}) \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/templates.py b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/templates.py new file mode 100644 index 00000000000..bd2a7482c65 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/templates.py @@ -0,0 +1,44 @@ +# 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 ChatPromptTemplate, MessagesPlaceholder + +query_rewrite_template = ChatPromptTemplate.from_messages([ + ("system", "Rewrite a query to a semantic search engine using the current conversation. " + "Provide only the rewritten query as output."), + MessagesPlaceholder(variable_name="messages") +]) + +rag_template = ChatPromptTemplate.from_messages([ + ("system", """You are an AI assistant for question-answering tasks. Follow these guidelines: +1. Use only the provided context to answer the question. +2. Give clear, accurate responses based on the information available. +3. If the context is insufficient, state: "I don't have enough information to answer this question." +4. Don't make assumptions or add information beyond the given context. +5. Use bullet points or lists for clarity when appropriate. +6. Briefly explain technical terms if necessary. +7. Use quotation marks for direct quotes from the context. + +Answer to the best of your ability using the context provided. +If you're unsure, it's better to acknowledge limitations than to speculate. + +## Context provided: +{% for doc in relevant_documents%} + +{{ doc.page_content | safe }} + +{% endfor %} +"""), + MessagesPlaceholder(variable_name="messages") +], template_format="jinja2") \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/vector_store.py b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/vector_store.py new file mode 100644 index 00000000000..1738ed8cba7 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/patterns/custom_rag_qa/vector_store.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. + +import logging +import os +from typing import List + +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.document_loaders import PyPDFLoader +from langchain_community.vectorstores import SKLearnVectorStore +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings + +PERSIST_PATH = ".persist_vector_store" +URL = "https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf" + + +def load_and_split_documents(url: str) -> List[Document]: + """Load and split documents from a given URL.""" + loader = PyPDFLoader(url) + documents = loader.load() + logging.info(f"# of documents loaded (pre-chunking) = {len(documents)}") + + text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) + doc_splits = text_splitter.split_documents(documents) + logging.info(f"# of documents after split = {len(doc_splits)}") + + return doc_splits + + +def get_vector_store( + embedding: Embeddings, persist_path: str = PERSIST_PATH, url: str = URL + ) -> SKLearnVectorStore: + """Get or create a vector store.""" + vector_store = SKLearnVectorStore(embedding=embedding, persist_path=persist_path) + + if not os.path.exists(persist_path): + doc_splits = load_and_split_documents(url=url) + vector_store.add_documents(documents=doc_splits) + vector_store.persist() + + return vector_store diff --git a/gemini/sample-apps/conversational-genai-app-template/app/patterns/langgraph_dummy_agent/chain.py b/gemini/sample-apps/conversational-genai-app-template/app/patterns/langgraph_dummy_agent/chain.py new file mode 100644 index 00000000000..507b65f4c00 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/patterns/langgraph_dummy_agent/chain.py @@ -0,0 +1,65 @@ +# 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 typing import Dict + +from langchain_core.messages import BaseMessage +from langchain_core.runnables import RunnableConfig +from langchain_core.tools import tool +from langchain_google_vertexai import ChatVertexAI +from langgraph.graph import END, MessagesState, StateGraph +from langgraph.prebuilt import ToolNode + + +# 1. Define tools +@tool +def search(query: str) -> str: + """Simulates a web search. Use it get information on weather""" + if "sf" in query.lower() or "san francisco" in query.lower(): + return "It's 60 degrees and foggy." + return "It's 90 degrees and sunny." + +tools = [search] + +# 2. Set up the language model +llm = ChatVertexAI( + model="gemini-1.5-pro-001", + temperature=0, + max_tokens=1024, + streaming=True +).bind_tools(tools) + +# 3. Define workflow components +def should_continue(state: MessagesState) -> str: + """Determines whether to use tools or end the conversation.""" + last_message = state['messages'][-1] + return "tools" if last_message.tool_calls else END # type: ignore[union-attr] + +async def call_model(state: MessagesState, config: RunnableConfig) -> Dict[str, BaseMessage]: + """Calls the language model and returns the response.""" + response = llm.invoke(state['messages'], config) + return {"messages": response} + +# 4. Create the workflow graph +workflow = StateGraph(MessagesState) +workflow.add_node("agent", call_model) +workflow.add_node("tools", ToolNode(tools)) +workflow.set_entry_point("agent") + +# 5. Define graph edges +workflow.add_conditional_edges("agent", should_continue) +workflow.add_edge("tools", 'agent') + +# 6. Compile the workflow +chain = workflow.compile() \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/server.py b/gemini/sample-apps/conversational-genai-app-template/app/server.py new file mode 100644 index 00000000000..63ce525fda1 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/server.py @@ -0,0 +1,108 @@ +# 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 json +import logging +import os +import uuid +from typing import AsyncGenerator + +from fastapi import FastAPI +from fastapi.responses import RedirectResponse, StreamingResponse +from google.cloud import logging as gcp_logging +from traceloop.sdk import Instruments, Traceloop + +from app.utils.input_types import Feedback, Input, InputChat, default_serialization +from app.utils.output_types import EndEvent, Event +from app.utils.tracing import CloudTraceLoggingSpanExporter + +# ruff: noqa: I001 +## Import the chain to be used +from app.chain import chain + +# Or choose one of the following pattern chains to test by uncommenting it: + +# Custom RAG QA +# from app.patterns.custom_rag_qa.chain import chain + +# Langgraph dummy agent +# from app.patterns.langgraph_dummy_agent.chain import chain + +# The events that are supported by the UI Frontend +SUPPORTED_EVENTS = ["on_tool_start", "on_tool_end", "on_retriever_start", + "on_retriever_end", "on_chat_model_stream"] + +# Initialize FastAPI app and logging +app = FastAPI() +logging_client = gcp_logging.Client() +logger = logging_client.logger(__name__) + +# Initialize Traceloop +try: + Traceloop.init( + app_name="Sample Chatbot Application", + disable_batch=False, + exporter=CloudTraceLoggingSpanExporter(), + instruments={Instruments.VERTEXAI, Instruments.LANGCHAIN}, + ) +except Exception as e: + logging.error("Failed to initialize Traceloop: %s", e) + + +async def stream_event_response(input_chat: InputChat) -> AsyncGenerator[str, None]: + run_id = uuid.uuid4() + input_dict = input_chat.model_dump() + Traceloop.set_association_properties({ + "log_type": "tracing", + "run_id": str(run_id), + "user_id": input_dict["user_id"], + "session_id": input_dict["session_id"], + "commit_sha": os.environ.get("COMMIT_SHA", "None") + }) + + yield json.dumps( + Event( + event="metadata", + data={"run_id": str(run_id)} + ), default=default_serialization) + "\n" + + async for data in chain.astream_events(input_dict, version="v2"): + if data["event"] in SUPPORTED_EVENTS: + yield json.dumps(data, default=default_serialization) + "\n" + + yield json.dumps(EndEvent(), default=default_serialization) + "\n" + + +# Routes +@app.get("/") +async def redirect_root_to_docs() -> RedirectResponse: + return RedirectResponse("/docs") + + +@app.post("/feedback") +async def collect_feedback(feedback_dict: Feedback) -> None: + logger.log_struct(feedback_dict.model_dump(), severity="INFO") + + +@app.post("/stream_events") +async def stream_chat_events(request: Input) -> StreamingResponse: + return StreamingResponse(stream_event_response(input_chat=request.input), + media_type="text/event-stream") + + +# Main execution +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/gemini/sample-apps/conversational-genai-app-template/app/utils/__init__.py b/gemini/sample-apps/conversational-genai-app-template/app/utils/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gemini/sample-apps/conversational-genai-app-template/app/utils/input_types.py b/gemini/sample-apps/conversational-genai-app-template/app/utils/input_types.py new file mode 100644 index 00000000000..0434fb09e92 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/utils/input_types.py @@ -0,0 +1,51 @@ +# 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 typing import Any, List, Literal, Optional, Union + +from langchain_core.messages import AIMessage, HumanMessage +from pydantic import BaseModel, Field + + +class InputChat(BaseModel): + """Represents the input for a chat session.""" + messages: List[Union[HumanMessage, AIMessage]] = Field( + ..., + description="The chat messages representing the current conversation." + ) + user_id: str = "" + session_id: str = "" + + +class Input(BaseModel): + """Wrapper class for InputChat.""" + input: InputChat + + +class Feedback(BaseModel): + """Represents feedback for a conversation.""" + score: Union[int, float] + text: Optional[str] = None + run_id: str + log_type: Literal['feedback'] = 'feedback' + + + +def default_serialization(obj: Any) -> Any: + """ + Default serialization for LangChain objects. + Converts BaseModel instances to dictionaries. + """ + if isinstance(obj, BaseModel): + return obj.model_dump() diff --git a/gemini/sample-apps/conversational-genai-app-template/app/utils/output_types.py b/gemini/sample-apps/conversational-genai-app-template/app/utils/output_types.py new file mode 100644 index 00000000000..21b26914007 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/utils/output_types.py @@ -0,0 +1,132 @@ +# 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 uuid +from functools import wraps +from types import GeneratorType +from typing import ( + Any, + AsyncGenerator, + Callable, + Dict, + List, + Literal, + Union, +) + +from langchain_core.documents import Document +from langchain_core.messages import AIMessage, AIMessageChunk +from pydantic import BaseModel, Field +from traceloop.sdk import TracerWrapper +from traceloop.sdk.decorators import workflow + + +class BaseCustomChainEvent(BaseModel): + name: Literal["custom_chain_event"] = "custom_chain_event" + + class Config: + extra = "allow" + + +class OnToolStartEvent(BaseCustomChainEvent): + event: Literal["on_tool_start"] = "on_tool_start" + input: Dict = {} + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + + +class ToolData(BaseModel): + input: Dict = {} + output: Union[List[Document], Dict] = {} + + +class OnToolEndEvent(BaseCustomChainEvent): + event: Literal["on_tool_end"] = "on_tool_end" + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + data: ToolData + + +class ChatModelStreamData(BaseModel): + chunk: AIMessageChunk + + +class OnChatModelStreamEvent(BaseCustomChainEvent): + event: Literal["on_chat_model_stream"] = "on_chat_model_stream" + data: ChatModelStreamData + + +class Event(BaseModel): + event: str = "data" + data: dict + + +class EndEvent(BaseModel): + event: Literal["end"] = "end" + + +class CustomChain: + """A custom chain class that wraps a callable function.""" + + def __init__(self, func: Callable): + """Initialize the CustomChain with a callable function.""" + self.func = func + + async def astream_events(self, *args: Any, **kwargs: Any) -> AsyncGenerator: + """ + Asynchronously stream events from the wrapped function. + Applies Traceloop workflow decorator if Traceloop SDK is initialized. + """ + + if hasattr(TracerWrapper, "instance"): + func = workflow()(self.func) + else: + func = self.func + + gen: GeneratorType = func(*args, **kwargs) + for event in gen: + yield event.model_dump() + + def invoke(self, *args: Any, **kwargs: Any) -> AIMessage: + """ + Invoke the wrapped function and process its events. + Returns an AIMessage with content and relative tool calls. + """ + events = self.func(*args, **kwargs) + response_content = "" + tool_calls = [] + for event in events: + if isinstance(event, OnChatModelStreamEvent): + if not isinstance(event.data.chunk.content, str): + raise ValueError("Chunk content must be a string") + response_content += event.data.chunk.content + elif isinstance(event, OnToolEndEvent): + tool_calls.append(event.data.model_dump()) + return AIMessage( + content=response_content, + additional_kwargs={"tool_calls_data": tool_calls} + ) + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + """Make the CustomChain instance callable, invoking the wrapped function.""" + return self.func(*args, **kwargs) + + +def custom_chain(func: Callable) -> CustomChain: + """ + Decorator function that wraps a callable in a CustomChain instance. + """ + @wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + return func(*args, **kwargs) + + return CustomChain(wrapper) \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/app/utils/tracing.py b/gemini/sample-apps/conversational-genai-app-template/app/utils/tracing.py new file mode 100644 index 00000000000..63fd4293fb8 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/app/utils/tracing.py @@ -0,0 +1,148 @@ +# 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 json +import logging +from typing import Any, Optional, Sequence + +from google.cloud import logging as gcp_logging +from google.cloud import storage +from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export import SpanExportResult + + +class CloudTraceLoggingSpanExporter(CloudTraceSpanExporter): + """ + An extended version of CloudTraceSpanExporter that logs span data to Google Cloud Logging + and handles large attribute values by storing them in Google Cloud Storage. + + This class helps bypass the 256 character limit of Cloud Trace for attribute values + by leveraging Cloud Logging (which has a 256KB limit) and Cloud Storage for larger payloads. + """ + def __init__( + self, + logging_client: Optional[gcp_logging.Client] = None, + storage_client: Optional[storage.Client] = None, + bucket_name: Optional[str] = None, + debug: bool = False, + **kwargs: Any + ) -> None: + """ + Initialize the exporter with Google Cloud clients and configuration. + + :param logging_client: Google Cloud Logging client + :param storage_client: Google Cloud Storage client + :param bucket_name: Name of the GCS bucket to store large payloads + :param debug: Enable debug mode for additional logging + :param kwargs: Additional arguments to pass to the parent class + """ + super().__init__(**kwargs) + self.debug = debug + self.logging_client = logging_client or gcp_logging.Client(project=self.project_id) + self.logger = self.logging_client.logger(__name__) + self.storage_client = storage_client or storage.Client(project=self.project_id) + self.bucket_name = bucket_name or f"{self.project_id}-logs-data" + self._ensure_bucket_exists() + self.bucket = self.storage_client.bucket(self.bucket_name) + + + def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult: + """ + Export the spans to Google Cloud Logging and Cloud Trace. + + :param spans: A sequence of spans to export + :return: The result of the export operation + """ + for span in spans: + span_context = span.get_span_context() + trace_id = format(span_context.trace_id, 'x') + span_id = format(span_context.span_id, 'x') + span_dict = json.loads(span.to_json()) + + span_dict["trace"] = f"projects/{self.project_id}/traces/{trace_id}" + span_dict["span_id"] = span_id + + span_dict = self._process_large_attributes( + span_dict=span_dict, span_id=span_id + ) + + if self.debug: + print(span_dict) + + # Log the span data to Google Cloud Logging + self.logger.log_struct(span_dict, severity="INFO") + + # Export spans to Google Cloud Trace using the parent class method + return super().export(spans) + + def _ensure_bucket_exists(self) -> None: + """Ensure that the GCS bucket exists, creating it if necessary.""" + if not self.storage_client.bucket(self.bucket_name).exists(): + logging.info(f"Bucket {self.bucket_name} not detected. Creating it now.") + self.storage_client.create_bucket(self.bucket_name) + + + def store_in_gcs(self, content: str, span_id: str) -> str: + """ + Initiate storing large content in Google Cloud Storage/ + + :param content: The content to store + :param span_id: The ID of the span + :return: The GCS URI of the stored content + """ + blob_name = f"spans/{span_id}.json" + blob = self.bucket.blob(blob_name) + + blob.upload_from_string(content, 'application/json') + return f"gs://{self.bucket_name}/{blob_name}" + + + def _process_large_attributes(self, span_dict: dict, span_id: str) -> dict: + """ + Process large attribute values by storing them in GCS if they exceed the size + limit of Google Cloud Logging. + + :param span_dict: The span data dictionary + :param trace_id: The trace ID + :param span_id: The span ID + :return: The updated span dictionary + """ + attributes = span_dict["attributes"] + if len(json.dumps(attributes).encode()) > 255 * 1024: # 250 KB + # Separate large payload from other attributes + attributes_payload = { + k: v for k, v in attributes.items() + if "traceloop.association.properties" not in k + } + attributes_retain = { + k: v for k, v in attributes.items() + if "traceloop.association.properties" in k + } + + # Store large payload in GCS + gcs_uri = self.store_in_gcs(json.dumps(attributes_payload), span_id) + attributes_retain["uri_payload"] = gcs_uri + attributes_retain["url_payload"] = ( + f"https://storage.mtls.cloud.google.com/" + f"{self.bucket_name}/spans/{span_id}.json" + ) + + span_dict["attributes"] = attributes_retain + logging.info( + "Length of payload span above 250 KB, storing attributes in GCS " + "to avoid large log entry errors" + ) + + return span_dict \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/README.md b/gemini/sample-apps/conversational-genai-app-template/deployment/README.md new file mode 100644 index 00000000000..ac00e931db6 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/README.md @@ -0,0 +1,106 @@ +## Deployment README.md +This folder contains the infrastructure-as-code and CI/CD pipeline configurations for deploying a conversational GenAI application on Google Cloud. + +The application leverages [**Terraform**](http://terraform.io) to define and provision the underlying infrastructure, while [**Cloud Build**](https://cloud.google.com/build/) orchestrates the continuous integration and continuous deployment (CI/CD) pipeline. + +### Deployment Workflow +![Deployment Workflow](../images/deployment_workflow.png) + +**Description:** + +1. CI Pipeline (`deployment/ci/pr_checks.yaml`): + - Triggered on pull request creation/update + - Runs unit and integration tests + +2. CD Pipeline (`deployment/cd/staging.yaml`): + - Triggered on merge to `main` branch + - Builds and pushes application to Artifact Registry + - Deploys to staging environment (Cloud Run) + - Performs load testing + +3. Production Deployment (`deployment/cd/deploy-to-prod.yaml`): + - Triggered after successful staging deployment + - Requires manual approval + - Deploys to production environment + + + +### Setup + +**Prerequisites:** + +1. A set of Google Cloud projects: + - Staging project + - Production project + - CI/CD project (can be the same as staging or production) +2. Terraform installed on your local machine +3. Enable required APIs in the CI/CD project. This will be required for the Terraform deployment: + ```bash + gcloud config set project YOUR_CI_CD_PROJECT_ID + gcloud services enable serviceusage.googleapis.com cloudresourcemanager.googleapis.com cloudbuild.googleapis.com secretmanager.googleapis.com + ``` + +### Step-by-Step Guide + +1. **Create a Git Repository using your favorite git provider (GitHub, GitLab, Bitbucket, etc.)** + +2. **Connect Your Repository to Cloud Build** + For detailed instructions, visit: [Cloud Build Repository Setup](https://cloud.google.com/build/docs/repositories#whats_next).
+ + ![Alt text](../images/connection_cb.gif) + +3. **Configure Terraform Variables** + - Edit [`deployment/terraform/vars/env.tfvars`](../terraform/vars/env.tfvars) with your GCP settings. + +| Variable | Description | Required | +|----------|-------------|:--------:| +| prod_project_id | **Production** GCP Project ID for resource deployment. | Yes | +| staging_project_id | **Staging** GCP Project ID for resource deployment. | Yes | +| cicd_runner_project_id | GCP Project ID where CI/CD pipelines will execute. | Yes | +| region | GCP region for resource deployment. | Yes | +| host_connection_name | Name of the host connection you created in Cloud Build | Yes | +| repository_name | Name of the repository you added to Cloud Build | Yes | + +Other optional variables include: telemetry and feedback BigQuery dataset IDs, log filters, sink names, service account names, bucket name suffixes, artifact registry repo name, and various role assignments for Cloud Run and CICD. + + +4. **Deploy Infrastructure with Terraform** + - Open a terminal and navigate to the Terraform directory: + ```bash + cd deployment/terraform + ``` + - Initialize Terraform: + ```bash + terraform init + ``` + - Apply the Terraform configuration: + ```bash + terraform apply --var-file vars/env.tfvars + ``` + - Type 'yes' when prompted to confirm + +After completing these steps, your infrastructure will be set up and ready for deployment! + + +### Dev Deployment +For end-to-end testing of the application, including tracing and feedback sinking to BigQuery, without the need to trigger a CI/CD pipeline. + +After you edited the relative [`env.tfvars` file](../terraform/dev/vars/env.tfvars), follow the following instructions: +```bash +cd deployment/terraform/dev +terraform init +terraform apply --var-file vars/env.tfvars +``` + +Then deploy the application using the following command (from the root of the repository): +```bash +gcloud run deploy conversational-app-sample --source . --project $YOUR_DEV_PROJECT_ID --service-account genai-app-sample-cr-sa@$YOUR_DEV_PROJECT_ID.iam.gserviceaccount.com +``` + + +**E2E Demo video** + + + Watch the video + + diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/cd/deploy-to-prod.yaml b/gemini/sample-apps/conversational-genai-app-template/deployment/cd/deploy-to-prod.yaml new file mode 100644 index 00000000000..7c51433df46 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/cd/deploy-to-prod.yaml @@ -0,0 +1,35 @@ +steps: + - name: 'gcr.io/cloud-builders/gcloud' + id: trigger-deployment + entrypoint: gcloud + args: + - 'run' + - 'deploy' + - 'genai-app-sample' + - '--image' + - 'us-central1-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME' + - '--region' + - 'us-central1' + - '--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}' + +substitutions: + _PROD_PROJECT_ID: YOUR_PROD_PROJECT_ID # Replace with 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 +options: + logging: CLOUD_LOGGING_ONLY \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/cd/staging.yaml b/gemini/sample-apps/conversational-genai-app-template/deployment/cd/staging.yaml new file mode 100644 index 00000000000..cbcb428e698 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/cd/staging.yaml @@ -0,0 +1,128 @@ +steps: +# # Build and Push + - name: 'gcr.io/cloud-builders/docker' + args: ['build', '-t', 'us-central1-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME', '.'] + - name: 'gcr.io/cloud-builders/docker' + args: ['push', 'us-central1-docker.pkg.dev/$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' + - 'us-central1-docker.pkg.dev/$PROJECT_ID/$_ARTIFACT_REGISTRY_REPO_NAME/$_CONTAINER_NAME' + - '--region' + - 'us-central1' + - '--project' + - '${_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}' + + # 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 us-central1 --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' + - '$LOCATION' + - '--project' + - '$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}' + - '--sha' + - $COMMIT_SHA + + - name: gcr.io/cloud-builders/gcloud + id: echo-view-build-trigger-link + entrypoint: /bin/bash + args: + - '-c' + - | + echo "_________________________________________________________________________" + echo "Production deployment triggered. View progress and / or approve on the Cloud Build Console:" + echo "https://console.cloud.google.com/cloud-build/builds;region=$LOCATION" + echo "_________________________________________________________________________" + + +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 + _CLOUD_RUN_APP_SA_NAME: genai-app-sample-cr-sa + +options: + substitutionOption: ALLOW_LOOSE + defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET +serviceAccount: projects/${PROJECT_ID}/serviceAccounts/cicd-runner@${PROJECT_ID}.iam.gserviceaccount.com \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/ci/pr_checks.yaml b/gemini/sample-apps/conversational-genai-app-template/deployment/ci/pr_checks.yaml new file mode 100644 index 00000000000..4bc5a05c28b --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/ci/pr_checks.yaml @@ -0,0 +1,27 @@ +steps: + - name: 'python:3.10' + id: install-dependencies + entrypoint: /bin/bash + args: + - '-c' + - | + pip install poetry==1.8.3 --user && python -m poetry install + + - name: 'python:3.10' + id: unit-tests + entrypoint: /bin/bash + args: + - '-c' + - | + python -m poetry run pytest tests/unit + + # Run integration tests + - name: 'python:3.10' + id: integration-tests + entrypoint: /bin/bash + args: + - '-c' + - | + python -m poetry run pytest tests/integration +options: + logging: CLOUD_LOGGING_ONLY \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/apis.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/apis.tf new file mode 100644 index 00000000000..43bdb00e0d9 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/apis.tf @@ -0,0 +1,47 @@ +locals { + cicd_services = [ + "cloudbuild.googleapis.com", + "discoveryengine.googleapis.com", + "aiplatform.googleapis.com", + "serviceusage.googleapis.com", + "bigquery.googleapis.com", + "cloudresourcemanager.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" + ] + + 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 + service = local.cicd_services[count.index] + disable_on_destroy = false +} + +resource "google_project_service" "shared_services" { + for_each = { + for pair in setproduct(keys(local.projects), local.shared_services) : + "${pair[0]}_${replace(pair[1], ".", "_")}" => { + project = local.projects[pair[0]] + service = pair[1] + } + } + project = each.value.project + service = each.value.service + disable_on_destroy = false +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/artifact_registry.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/artifact_registry.tf new file mode 100644 index 00000000000..fed1c2c7306 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/artifact_registry.tf @@ -0,0 +1,8 @@ +resource "google_artifact_registry_repository" "my-repo" { + location = "us-central1" + repository_id = var.artifact_registry_repo_name + description = "Repo for GenAI applications" + format = "DOCKER" + project = var.cicd_runner_project_id + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/build_triggers.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/build_triggers.tf new file mode 100644 index 00000000000..7f5d004153c --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/build_triggers.tf @@ -0,0 +1,80 @@ +# a. Create PR checks trigger +resource "google_cloudbuild_trigger" "pr_checks" { + name = "pr-checks" + project = var.cicd_runner_project_id + location = var.region + description = "Trigger for PR checks" + service_account = resource.google_service_account.cicd_runner_sa.id + + repository_event_config { + repository = "projects/${var.cicd_runner_project_id}/locations/${var.region}/connections/${var.host_connection_name}/repositories/${var.repository_name}" + pull_request { + branch = "main" + } + } + + filename = "deployment/ci/pr_checks.yaml" + included_files = [ + "app/**", + "tests/**", + "deployment/**", + "poetry.lock" + ] + + include_build_logs = "INCLUDE_BUILD_LOGS_WITH_STATUS" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +# b. Create CD pipeline trigger +resource "google_cloudbuild_trigger" "cd_pipeline" { + name = "cd-pipeline" + project = var.cicd_runner_project_id + location = var.region + service_account = resource.google_service_account.cicd_runner_sa.id + description = "Trigger for CD pipeline" + + repository_event_config { + repository = "projects/${var.cicd_runner_project_id}/locations/${var.region}/connections/${var.host_connection_name}/repositories/${var.repository_name}" + push { + branch = "main" + } + } + + filename = "deployment/cd/staging.yaml" + included_files = [ + "app/**", + "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 + } + + include_build_logs = "INCLUDE_BUILD_LOGS_WITH_STATUS" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} + +# c. Create Deploy to production trigger +resource "google_cloudbuild_trigger" "deploy_to_prod_pipeline" { + name = "deploy-to-prod-pipeline" + project = var.cicd_runner_project_id + location = var.region + description = "Trigger for deployment to production" + service_account = resource.google_service_account.cicd_runner_sa.id + repository_event_config { + repository = "projects/${var.cicd_runner_project_id}/locations/${var.region}/connections/${var.host_connection_name}/repositories/${var.repository_name}" + } + filename = "deployment/cd/deploy-to-prod.yaml" + include_build_logs = "INCLUDE_BUILD_LOGS_WITH_STATUS" + approval_config { + approval_required = true + } + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/iam.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/iam.tf new file mode 100644 index 00000000000..9b17722d1cd --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/iam.tf @@ -0,0 +1,20 @@ +locals { + project_ids = { + dev = var.dev_project_id + } +} + +# 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) : + join(",", pair) => { + project = local.project_ids[pair[0]] + role = pair[1] + } + } + + project = each.value.project + role = each.value.role + member = "serviceAccount:${google_service_account.cloud_run_app_sa.email}" + } diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/log_sinks.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/log_sinks.tf new file mode 100644 index 00000000000..abe3bb9fc34 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/log_sinks.tf @@ -0,0 +1,49 @@ +resource "google_project_iam_member" "bigquery_data_editor" { + + project = var.dev_project_id + role = "roles/bigquery.dataEditor" + member = module.log_export_to_bigquery.writer_identity +} + + +module "log_export_to_bigquery" { + + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + + log_sink_name = var.telemetry_sink_name + parent_resource_type = "project" + parent_resource_id = var.dev_project_id + destination_uri = "bigquery.googleapis.com/projects/${var.dev_project_id}/datasets/${var.telemetry_bigquery_dataset_id}" + filter = var.telemetry_logs_filter + bigquery_options = { use_partitioned_tables = true } + unique_writer_identity = true + +} + +resource "google_bigquery_dataset" "feedback_dataset" { + project = var.dev_project_id + dataset_id = var.feedback_bigquery_dataset_id + friendly_name = var.feedback_bigquery_dataset_id + location = var.region + +} + +module "feedback_export_to_bigquery" { + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + log_sink_name = var.feedback_sink_name + parent_resource_type = "project" + parent_resource_id = var.dev_project_id + destination_uri = "bigquery.googleapis.com/projects/${var.dev_project_id}/datasets/${var.feedback_bigquery_dataset_id}" + filter = var.feedback_logs_filter + bigquery_options = { use_partitioned_tables = true } + unique_writer_identity = true +} + +resource "google_bigquery_dataset" "telemetry_logs_dataset" { + project = var.dev_project_id + dataset_id = var.telemetry_bigquery_dataset_id + friendly_name = var.telemetry_bigquery_dataset_id + location = var.region +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/service_accounts.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/service_accounts.tf new file mode 100644 index 00000000000..cbf04d39f44 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/service_accounts.tf @@ -0,0 +1,5 @@ +resource "google_service_account" "cloud_run_app_sa" { + account_id = var.cloud_run_app_sa_name + display_name = "Cloud Run GenAI app SA" + project = var.dev_project_id +} diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/storage.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/storage.tf new file mode 100644 index 00000000000..5aa56e17dc8 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/storage.tf @@ -0,0 +1,18 @@ +resource "google_storage_bucket" "logs_data_bucket" { + name = "${var.dev_project_id}-logs-data" + location = var.region + project = var.dev_project_id + uniform_bucket_level_access = true + + lifecycle { + prevent_destroy = true + ignore_changes = all + } + + # Use this block to create the bucket only if it doesn't exist + count = length(data.google_storage_bucket.existing_bucket) > 0 ? 0 : 1 +} + +data "google_storage_bucket" "existing_bucket" { + name = "${var.dev_project_id}-logs-data" +} diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/variables.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/variables.tf new file mode 100644 index 00000000000..c2f8d6f95b7 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/variables.tf @@ -0,0 +1,64 @@ +variable "dev_project_id" { + type = string + description = "**Dev** GCP Project ID for resource deployment." +} + +variable "region" { + type = string + description = "GCP region for resource deployment." + default = "us-central1" +} + +variable "telemetry_bigquery_dataset_id" { + type = string + description = "BigQuery dataset ID for telemetry data export." + default = "telemetry_genai_app_sample_sink" +} + +variable "feedback_bigquery_dataset_id" { + type = string + description = "BigQuery dataset ID for feedback data export." + default = "feedback_genai_app_sample_sink" +} + +variable "telemetry_logs_filter" { + type = string + description = "Log Sink filter for capturing telemetry data. Captures logs with the `traceloop.association.properties.log_type` attribute set to `tracing`." + default = "jsonPayload.attributes.\"traceloop.association.properties.log_type\"=\"tracing\" jsonPayload.resource.attributes.\"service.name\"=\"Sample Chatbot Application\"" +} + +variable "feedback_logs_filter" { + type = string + description = "Log Sink filter for capturing feedback data. Captures logs where the `log_type` field is `feedback`." + default = "jsonPayload.log_type=\"feedback\"" +} + +variable "telemetry_sink_name" { + type = string + description = "Name of the telemetry data Log Sink." + default = "telemetry_logs_genai_app_sample" +} + +variable "feedback_sink_name" { + type = string + description = "Name of the feedback data Log Sink." + default = "feedback_logs_genai_app_sample" +} + +variable "cloud_run_app_sa_name" { + description = "Service account name to be used for the Cloud Run service" + type = string + default = "genai-app-sample-cr-sa" +} + +variable "cloud_run_app_roles" { + description = "List of roles to assign to the Cloud Run app service account" + type = list(string) + default = [ + "roles/aiplatform.user", + "roles/discoveryengine.editor", + "roles/logging.logWriter", + "roles/cloudtrace.agent", + "roles/storage.admin" + ] +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/vars/env.tfvars b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/vars/env.tfvars new file mode 100644 index 00000000000..f3b70680551 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/dev/vars/env.tfvars @@ -0,0 +1,16 @@ +# Your Dev GCP project id +dev_project_id = "your-dev-project-id" + +# The GCP region you will use to deploy the infrastructure +region = "us-central1" + +telemetry_bigquery_dataset_id = "telemetry_genai_app_sample_sink" +telemetry_sink_name = "telemetry_logs_genai_app_sample" +telemetry_logs_filter = "jsonPayload.attributes.\"traceloop.association.properties.log_type\"=\"tracing\" jsonPayload.resource.attributes.\"service.name\"=\"Sample Chatbot Application\"" + +feedback_bigquery_dataset_id = "feedback_genai_app_sample_sink" +feedback_sink_name = "feedback_logs_genai_app_sample" +feedback_logs_filter = "jsonPayload.log_type=\"feedback\"" + +cloud_run_app_sa_name = "genai-app-sample-cr-sa" + diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/iam.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/iam.tf new file mode 100644 index 00000000000..b94c1f6017d --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/iam.tf @@ -0,0 +1,81 @@ +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 + project_id = each.value +} + +# 1. Assign roles for the CICD project +resource "google_project_iam_member" "cicd_project_roles" { + for_each = toset(var.cicd_roles) + + project = var.cicd_runner_project_id + role = each.value + member = "serviceAccount:${resource.google_service_account.cicd_runner_sa.email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} + +# 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) : + "${pair[0]}-${pair[1]}" => { + project_id = local.project_ids[pair[0]] + role = pair[1] + } + } + + project = each.value.project_id + role = each.value.role + member = "serviceAccount:${resource.google_service_account.cicd_runner_sa.email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +# 3. Allow Cloud Run service SA to pull containers stored in the CICD project +resource "google_project_iam_member" "cicd_run_invoker_artifact_registry_reader" { + for_each = local.project_ids + project = var.cicd_runner_project_id + + role = "roles/artifactregistry.reader" + member = "serviceAccount:service-${data.google_project.projects[each.key].number}@serverless-robot-prod.iam.gserviceaccount.com" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} + +# 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) : + join(",", pair) => { + project = local.project_ids[pair[0]] + role = pair[1] + } + } + + project = each.value.project + role = each.value.role + member = "serviceAccount:${google_service_account.cloud_run_app_sa[split(",", each.key)[0]].email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +# Special assignment: Allow the CICD SA to create tokens +resource "google_service_account_iam_member" "cicd_run_invoker_token_creator" { + service_account_id = google_service_account.cicd_runner_sa.name + role = "roles/iam.serviceAccountTokenCreator" + member = "serviceAccount:${resource.google_service_account.cicd_runner_sa.email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} +# Special assignment: Allow the CICD SA to impersonate himself for trigger creation +resource "google_service_account_iam_member" "cicd_run_invoker_account_user" { + service_account_id = google_service_account.cicd_runner_sa.name + role = "roles/iam.serviceAccountUser" + member = "serviceAccount:${resource.google_service_account.cicd_runner_sa.email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/log_sinks.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/log_sinks.tf new file mode 100644 index 00000000000..8a512d7f05b --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/log_sinks.tf @@ -0,0 +1,58 @@ +resource "google_project_iam_member" "bigquery_data_editor" { + for_each = local.project_ids + + project = each.value + role = "roles/bigquery.dataEditor" + member = module.log_export_to_bigquery[each.key].writer_identity +} + +module "log_export_to_bigquery" { + for_each = local.project_ids + + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + + log_sink_name = var.telemetry_sink_name + parent_resource_type = "project" + parent_resource_id = each.value + destination_uri = "bigquery.googleapis.com/projects/${each.value}/datasets/${var.telemetry_bigquery_dataset_id}" + filter = var.telemetry_logs_filter + bigquery_options = { use_partitioned_tables = true } + unique_writer_identity = true + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} + +resource "google_bigquery_dataset" "feedback_dataset" { + for_each = local.project_ids + project = each.value + dataset_id = var.feedback_bigquery_dataset_id + friendly_name = var.feedback_bigquery_dataset_id + location = var.region + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] + +} + +module "feedback_export_to_bigquery" { + for_each = local.project_ids + + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + log_sink_name = var.feedback_sink_name + parent_resource_type = "project" + parent_resource_id = each.value + destination_uri = "bigquery.googleapis.com/projects/${each.value}/datasets/${var.feedback_bigquery_dataset_id}" + filter = var.feedback_logs_filter + bigquery_options = { use_partitioned_tables = true } + unique_writer_identity = true + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services, google_bigquery_dataset.feedback_dataset] +} + +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 + project = each.value + dataset_id = var.telemetry_bigquery_dataset_id + friendly_name = var.telemetry_bigquery_dataset_id + location = var.region +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/service_accounts.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/service_accounts.tf new file mode 100644 index 00000000000..1e2b8fa8753 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/service_accounts.tf @@ -0,0 +1,15 @@ +resource "google_service_account" "cicd_runner_sa" { + account_id = var.cicd_runner_sa_name + display_name = "CICD Runner SA" + project = var.cicd_runner_project_id + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +resource "google_service_account" "cloud_run_app_sa" { + for_each = local.project_ids + + account_id = var.cloud_run_app_sa_name + display_name = "Cloud Run GenAI 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/conversational-genai-app-template/deployment/terraform/storage.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/storage.tf new file mode 100644 index 00000000000..63cbbb541ab --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/storage.tf @@ -0,0 +1,27 @@ +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}-${var.suffix_bucket_name_load_test_results}" + location = var.region + project = var.cicd_runner_project_id + uniform_bucket_level_access = true + force_destroy = true + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} + +resource "google_storage_bucket" "logs_data_bucket" { + for_each = toset(local.all_projects) + name = "${each.value}-logs-data" + 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/conversational-genai-app-template/deployment/terraform/variables.tf b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/variables.tf new file mode 100644 index 00000000000..b4c48076c43 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/variables.tf @@ -0,0 +1,125 @@ +variable "prod_project_id" { + type = string + description = "**Production** GCP Project ID for resource deployment." +} + +variable "staging_project_id" { + type = string + description = "**Staging** GCP Project ID for resource deployment." +} + +variable "cicd_runner_project_id" { + type = string + description = "GCP Project ID where CI/CD pipelines will execute." +} + +variable "region" { + type = string + description = "GCP region for resource deployment." + default = "us-central1" +} + +variable "host_connection_name" { + description = "Name of the host connection you created in Cloud Build" + type = string +} + +variable "repository_name" { + description = "Name of the repository you'd like to connect to Cloud Build" + type = string +} + +variable "telemetry_bigquery_dataset_id" { + type = string + description = "BigQuery dataset ID for telemetry data export." + default = "telemetry_genai_app_sample_sink" +} + +variable "feedback_bigquery_dataset_id" { + type = string + description = "BigQuery dataset ID for feedback data export." + default = "feedback_genai_app_sample_sink" +} + +variable "telemetry_logs_filter" { + type = string + description = "Log Sink filter for capturing telemetry data. Captures logs with the `traceloop.association.properties.log_type` attribute set to `tracing`." + default = "jsonPayload.attributes.\"traceloop.association.properties.log_type\"=\"tracing\" jsonPayload.resource.attributes.\"service.name\"=\"Sample Chatbot Application\"" +} + +variable "feedback_logs_filter" { + type = string + description = "Log Sink filter for capturing feedback data. Captures logs where the `log_type` field is `feedback`." + default = "jsonPayload.log_type=\"feedback\"" +} + +variable "telemetry_sink_name" { + type = string + description = "Name of the telemetry data Log Sink." + default = "telemetry_logs_genai_app_sample" +} + +variable "feedback_sink_name" { + type = string + description = "Name of the feedback data Log Sink." + default = "feedback_logs_genai_app_sample" +} + +variable "cicd_runner_sa_name" { + description = "Service account name to be used for the CICD processes" + type = string + default = "cicd-runner" +} + +variable "cloud_run_app_sa_name" { + description = "Service account name to be used for the Cloud Run service" + type = string + default = "genai-app-sample-cr-sa" +} + +variable "suffix_bucket_name_load_test_results" { + description = "Suffix Name of the bucket that will be used to store the results of the load test. Prefix will be project id." + type = string + default = "cicd-load-test-results" +} + + +variable "artifact_registry_repo_name" { + description = "Name of the Artifact registry repository to be used to push containers" + type = string + default = "genai-containers" +} + + + +variable "cloud_run_app_roles" { + description = "List of roles to assign to the Cloud Run app service account" + type = list(string) + default = [ + "roles/aiplatform.user", + "roles/discoveryengine.editor", + "roles/logging.logWriter", + "roles/cloudtrace.agent", + "roles/storage.admin" + ] +} + +variable "cicd_roles" { + description = "List of roles to assign to the CICD runner service account in the CICD project" + type = list(string) + default = [ + "roles/storage.admin", + "roles/run.invoker", + "roles/aiplatform.user", + "roles/discoveryengine.editor", + "roles/logging.logWriter", + "roles/artifactregistry.writer", + "roles/cloudbuild.builds.builder" + ] +} + +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"] +} \ No newline at end of file diff --git a/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/vars/env.tfvars b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/vars/env.tfvars new file mode 100644 index 00000000000..21f914c0606 --- /dev/null +++ b/gemini/sample-apps/conversational-genai-app-template/deployment/terraform/vars/env.tfvars @@ -0,0 +1,31 @@ +# Your Production GCP project id +prod_project_id = "your-production-project-id" + +# Your Staging / Test GCP project id +staging_project_id = "your-staging-project-id" + +# Your GCP project ID that will be used to host the Cloud Build pipelines. +cicd_runner_project_id = "your-cicd-project-id" + +# Name of the host connection you created in Cloud Build +host_connection_name = "your-host-connection-name" + +# Name of the repository you added to Cloud Build +repository_name = "your-repository-name" + +# The GCP region you will use to deploy the infrastructure +region = "us-central1" + +telemetry_bigquery_dataset_id = "telemetry_genai_app_sample_sink" +telemetry_sink_name = "telemetry_logs_genai_app_sample" +telemetry_logs_filter = "jsonPayload.attributes.\"traceloop.association.properties.log_type\"=\"tracing\" jsonPayload.resource.attributes.\"service.name\"=\"Sample Chatbot Application\"" + +feedback_bigquery_dataset_id = "feedback_genai_app_sample_sink" +feedback_sink_name = "feedback_logs_genai_app_sample" +feedback_logs_filter = "jsonPayload.log_type=\"feedback\"" + +cicd_runner_sa_name = "cicd-runner" +cloud_run_app_sa_name = "genai-app-sample-cr-sa" + +suffix_bucket_name_load_test_results = "cicd-load-test-results" +artifact_registry_repo_name = "genai-containers" \ No newline at end of file diff --git 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