diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 1a5743e4c9..3d9014297d 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -73,3 +73,4 @@ /generative-ai/open-models/serving/vertex_ai_text_generation_inference_gemma.ipynb @alvarobartt @philschmid @pagezyhf @jeffboudier /generative-ai/gemini/use-cases/applying-llms-to-data/semantic-search-in-bigquery/stackoverflow_questions_semantic_search.ipynb @sethijaideep @GoogleCloudPlatform/generative-ai-devrel /generative-ai/gemini/use-cases/retrieval-augmented-generation/raw_with_bigquery.ipynb @jeffonelson @GoogleCloudPlatform/generative-ai-devrel +/generative-ai/gemini/sample-apps/e2e-gen-ai-app-starter-kit @eliasecchig @lspatarog @GoogleCloudPlatform/generative-ai-devrel diff --git a/.github/actions/spelling/allow.txt b/.github/actions/spelling/allow.txt index 084936e4cc..d2e0b30c3b 100644 --- a/.github/actions/spelling/allow.txt +++ b/.github/actions/spelling/allow.txt @@ -144,6 +144,7 @@ Kohli Kraizt Kubeflow Kudrow +LangGraph LCEL LLMs LOOKBACK @@ -202,6 +203,7 @@ Oort PDFs PEFT PLOTLYENV +PYINK Parmar Persero Phaidon @@ -270,6 +272,7 @@ Testables Tetsuo Tianli Topolino +Traceloop Trapp Tribbiani Tricyle @@ -360,6 +363,7 @@ cfbundle chatbots chromadb claude +cicd clickable cmap codebase @@ -367,6 +371,7 @@ codebases codefile codelab codelabs +codespell colab coldline coloraxis @@ -473,6 +478,7 @@ gsutil gtk guanciale gunicorn +hadolint hashtag hashtags hdlr @@ -671,11 +677,13 @@ sxs tabular tagline tencel +terraform termcolor terraform textno tfhub tfidf +tfvars tgz thelook tiktoken @@ -683,7 +691,9 @@ timechart titlebar tobytes toself +toset tqdm +traceloop tritan tsv ubuntu @@ -698,6 +708,7 @@ usernames uvb uvicorn vapo +uvicorn vectordb vertexai vesselin diff --git a/.github/workflows/_e2e_gen_ai_app_starter_kit__lint_and_test.yaml b/.github/workflows/_e2e_gen_ai_app_starter_kit__lint_and_test.yaml new file mode 100644 index 0000000000..edf0ae71af --- /dev/null +++ b/.github/workflows/_e2e_gen_ai_app_starter_kit__lint_and_test.yaml @@ -0,0 +1,35 @@ +name: Conversational Gen AI template - Lint and Test + +on: + push: + paths: + - "gemini/sample-apps/e2e-gen-ai-app-starter-kit/**" + pull_request: + paths: + - "gemini/sample-apps/e2e-gen-ai-app-starter-kit/**" + +jobs: + lint-and-test: + runs-on: ubuntu-latest + defaults: + run: + working-directory: gemini/sample-apps/e2e-gen-ai-app-starter-kit + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.10" + - name: Install Poetry and dependencies + run: | + pip install poetry==1.8.3 --user + python -m poetry install --with lint,streamlit + - name: Lint python code + run: | + poetry run codespell + poetry run ruff . + poetry run mypy . + poetry run black . --check + - name: Run tests + run: | + poetry run pytest tests/unit diff --git a/.github/workflows/linter.yaml b/.github/workflows/linter.yaml index eddd527e88..bd246bd233 100644 --- a/.github/workflows/linter.yaml +++ b/.github/workflows/linter.yaml @@ -52,7 +52,7 @@ jobs: env: DEFAULT_BRANCH: main GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - FILTER_REGEX_EXCLUDE: 'gemini/sample-apps/finance-advisor-spanner/.*\.sql' + FILTER_REGEX_EXCLUDE: 'gemini/sample-apps/finance-advisor-spanner/.*\.sql|gemini/sample-apps/e2e-gen-ai-app-starter-kit/.*' JAVASCRIPT_DEFAULT_STYLE: prettier LOG_LEVEL: WARN SHELLCHECK_OPTS: -e SC1091 -e 2086 diff --git a/.gitignore b/.gitignore index f516bbe605..65cf5faa88 100644 --- a/.gitignore +++ b/.gitignore @@ -144,6 +144,7 @@ dmypy.json # User-specific files language/examples/prompt-design/train.csv README-TOC*.md +gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/terraform.tfstate* .terraform* .Terraform* diff --git a/core b/core new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gemini/sample-apps/README.md b/gemini/sample-apps/README.md index e0c7648622..579427309b 100644 --- a/gemini/sample-apps/README.md +++ b/gemini/sample-apps/README.md @@ -16,3 +16,4 @@ We provide instructions for setting up your environment in [Cloud Shell](https:/ | Deploy a RAG + Gemini sample application to troubleshoot your car using the owner's manual. | [fixmycar/](fixmycar/) | Chat, Grounding, RAG, Java, Streamlit | | Try Gemini image recognition in `bash` and see Text-to-Speech read the description to you in ~any language. All from CLI! | [image-bash-jam/](image-bash-jam/) | Text-to-Speech, Bash | | This demo showcases how you can combine the data and documents you already have and the skills you already know with the power of [AlloyDB AI](https://cloud.google.com/alloydb/ai?hl=en), [Vertex AI](https://cloud.google.com/vertex-ai?hl=en), [Cloud Run](https://cloud.google.com/run?hl=en), and [Cloud Functions](https://cloud.google.com/functions?hl=en) to build trustworthy Gen AI features into your existing applications. | [GenWealth](genwealth/) | Vertex AI, AlloyDB, Document AI, Cloud Run, Cloud Functions, Cloud Storage | +| E2E Gen AI App Starter kit: This folder provides a template starter kit for building a Generative AI application on Google Cloud. We provide a comprehensive set of resources to guide you through the entire development process, from prototype to production. | [e2e-gen-ai-app-starter-kit](e2e-gen-ai-app-starter-kit/) | Vertex AI, FastAPI, LangChain, Cloud Run, Cloud Build, Terraform, Streamlit | diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/.gitignore b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/.gitignore new file mode 100644 index 0000000000..bf43be5cf5 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/.gitignore @@ -0,0 +1,194 @@ +# 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 +.terraform* +.Terraform* + + +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/e2e-gen-ai-app-starter-kit/CONTRIBUTING.md b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/CONTRIBUTING.md new file mode 100644 index 0000000000..c7891306d0 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/CONTRIBUTING.md @@ -0,0 +1,47 @@ +# 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 [Google Developers CLA](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/e2e-gen-ai-app-starter-kit/Dockerfile b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/Dockerfile new file mode 100644 index 0000000000..c103b88d30 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.11-slim + +RUN pip install --no-cache-dir 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 ["uvicorn", "app.server:app", "--host", "0.0.0.0", "--port", "8080"] \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/Makefile b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/Makefile new file mode 100644 index 0000000000..d4d337073a --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/Makefile @@ -0,0 +1,20 @@ +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 . + poetry run black . diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/README.md new file mode 100644 index 0000000000..25eac3a4eb --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/README.md @@ -0,0 +1,203 @@ +# πŸš€ E2E Gen AI App Starter kit! πŸš€ + +> **From Prototype to Production in Minutes.** + +| | | +|-|-| +| Authors | [Elia Secchi](https://github.com/eliasecchig) [Lorenzo Spataro](https://github.com/lspataroG) | + +This repository provides a template starter kit for building a Generative AI application on Google Cloud. + +We provide a comprehensive set of resources to guide you through the entire development process, from prototype to production. + +This is a suggested approach, and **you can adapt it to fit your specific needs and preferences**. There are multiple ways to build Gen AI applications on Google Cloud, and this template serves as a starting point and example. + +## High-Level Architecture + +This starter kit covers all aspects of Generative AI 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 starter kit 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) | + +
+ +
+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 Generative AI applications using tools like Vertex AI rapid eval SDK and Vertex AI Experiments. | ![Vertex AI Rapid Eval](images/vertex_ai_rapid_eval.png) | + +
+ +
+Unlock Insights with Google Cloud Native Tracing & Logging + +| Description | Visualization | +| ------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | +| Seamlessly integrate with OpenTelemetry, Cloud Trace, Cloud Logging and BigQuery for comprehensive data collection and log every step of your Gen AI application to unlock powerful insights. | ![Tracing Preview](images/tracing_preview.png) | + +
+ +
+ +Monitor Responses from the application + +| Description | Visualization | +| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------- | +| Monitor your Generative AI 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 template includes implementation of unit, integration and load tests and a set of terraform resources for you to setup your own Google Cloud project in a matter of minutes. | ![cicd](images/cicd.png) | + +
+ +
+A comprehensive UI Playground + +| Description | Visualization | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | +| Experiment with your Generative AI 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://e2e-gen-ai-app-starter-kit/repository.zip . && unzip repository.zip +``` + +Use the downloaded folder as a starting point for your own Generative AI application. + +### Installation + +Install required packages using Poetry: + +```bash +poetry install --with streamlit,jupyter +``` + +### Setup + +Set your default Google Cloud 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 Generative AI 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 Generative AI 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 Google Cloud 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). + +## Feedback + +We value your input! Your feedback helps us improve this starter kit and make it more useful for the community. + +### Getting Help + +If you encounter any issues or have specific suggestions, please first consider [raising an issue](https://github.com/GoogleCloudPlatform/generative-ai/issues) on our GitHub repository. + +### Share Your Experience + +For other types of feedback, or if you'd like to share a positive experience or success story using this starter kit, we'd love to hear from you! You can reach out to us at [e2e-gen-ai-app-starter-kit@google.com](mailto:e2e-gen-ai-app-starter-kit@google.com). + +Thank you for your contributions! + +## 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/e2e-gen-ai-app-starter-kit/app/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/README.md new file mode 100644 index 0000000000..247f1422ce --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/README.md @@ -0,0 +1,65 @@ +# 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 + +```plaintext +. +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 +``` + +## Generative AI Application Patterns + +### 1. Default Chain + +The default chain is a simple conversational bot that produces recipes based on user questions. + +### 2. Custom RAG QA + +A RAG (Retrieval-Augmented Generation) chain using Python for orchestration and base LangChain components. The chain demonstrates how to create a production-grade application with full control over the orchestration process. + +This approach offers maximum flexibility in the orchestration of steps and allows for seamless integration with other SDK frameworks such as [Vertex AI SDK](https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk) and [LangChain](https://python.langchain.com/), retaining the support to emit `astream_events` [API compatible events](https://python.langchain.com/docs/how_to/streaming/#using-stream-events). + +### 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](https://lookerstudio.google.com/c/reporting/fa742264-4b4b-4c56-81e6-a667dd0f853f/page/tEnnC). + +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/e2e-gen-ai-app-starter-kit/app/__init__.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/chain.py new file mode 100644 index 0000000000..d367bd1ad7 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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 + +llm = ChatVertexAI( + model_name="gemini-1.5-flash-002", + temperature=0, + max_output_tokens=1024, +) + + +template = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a knowledgeable culinary assistant specializing in providing" + "detailed cooking recipes. Your responses should be informative, engaging, " + "and tailored to the user's specific requests. Include ingredients, " + "step-by-step instructions, cooking times, and any helpful tips or " + "variations. If asked about dietary restrictions or substitutions, offer " + "appropriate alternatives.", + ), + MessagesPlaceholder(variable_name="messages"), + ] +) + +chain = template | llm diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/eval/data/chats.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/eval/data/chats.yaml new file mode 100644 index 0000000000..1eb4f9bdb6 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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. diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/eval/utils.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/eval/utils.py new file mode 100644 index 0000000000..32e63fbbf1 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/eval/utils.py @@ -0,0 +1,244 @@ +# 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 + [ + {"type": "human", "content": "user's message"}, + {"type": "ai", "content": "AI's response"}, + {"type": "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": [ + [ + {"type": "human", "content": "What's the weather today?"} + ], + [ + {"type": "human", "content": "Tell me a joke."}, + {"type": "ai", "content": "Why did the scarecrow win an award?"}, + {"type": "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/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/chain.py new file mode 100644 index 0000000000..9c304b6821 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/chain.py @@ -0,0 +1,145 @@ +# 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,attr-defined" +import logging +from typing import Any, Dict, Iterator, List + +import google +import vertexai +from langchain.schema import Document +from langchain.tools import tool +from langchain_core.messages import ToolMessage +from langchain_google_community.vertex_rank import VertexAIRank +from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings + +from app.patterns.custom_rag_qa.templates import ( + inspect_conversation_template, + rag_template, + template_docs, +) +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-002" +TOP_K = 5 + +# Initialize logging +logging.basicConfig(level=logging.INFO) + +# Initialize Google Cloud and Vertex AI +credentials, project_id = google.auth.default() +vertexai.init(project=project_id) + +# Set up embedding model and vector store +embedding = VertexAIEmbeddings(model_name=EMBEDDING_MODEL) +vector_store = get_vector_store(embedding=embedding) +retriever = vector_store.as_retriever(search_kwargs={"k": 20}) + +# Initialize document compressor +compressor = VertexAIRank( + project_id=project_id, + location_id="global", + ranking_config="default_ranking_config", + title_field="id", + top_n=TOP_K, +) + + +@tool +def retrieve_docs(query: str) -> List[Document]: + """ + Useful for retrieving relevant documents based on a query. + Use this when you need additional information to answer a question. + + Args: + query (str): The user's question or search query. + + Returns: + List[Document]: A list of the top-ranked Document objects, limited to TOP_K (5) results. + """ + retrieved_docs = retriever.invoke(query) + ranked_docs = compressor.compress_documents(documents=retrieved_docs, query=query) + return ranked_docs + + +@tool +def should_continue() -> None: + """ + Use this tool if you determine that you have enough context to respond to the questions of the user. + """ + return None + + +# Initialize language model +llm = ChatVertexAI(model=LLM_MODEL, temperature=0, max_tokens=1024) + +# Set up conversation inspector +inspect_conversation = inspect_conversation_template | llm.bind_tools( + [retrieve_docs, should_continue], tool_choice="any" +) + +# Set up response chain +response_chain = rag_template | llm + + +@custom_chain +def chain( + input: Dict[str, Any], **kwargs: Any +) -> Iterator[OnToolEndEvent | OnChatModelStreamEvent]: + """ + Implement a RAG QA chain with tool calls. + + This function is decorated with `custom_chain` to offer LangChain compatible + astream_events, support for synchronous invocation through the `invoke` method, + and OpenTelemetry tracing. + """ + # Inspect conversation and determine next action + inspection_result = inspect_conversation.invoke(input) + tool_call_result = inspection_result.tool_calls[0] + + # Execute the appropriate tool based on the inspection result + if tool_call_result["name"] == "retrieve_docs": + # Retrieve relevant documents + docs = retrieve_docs.invoke(tool_call_result["args"]) + # Format the retrieved documents + formatted_docs = template_docs.format(docs=docs) + # Create a ToolMessage with the formatted documents + tool_message = ToolMessage( + tool_call_id=tool_call_result["name"], + name=tool_call_result["name"], + content=formatted_docs, + artifact=docs, + ) + else: + # If no documents need to be retrieved, continue with the conversation + tool_message = should_continue.invoke(tool_call_result) + + # Update input messages with new information + input["messages"] = input["messages"] + [inspection_result, tool_message] + + # Yield tool results metadata + yield OnToolEndEvent( + data={"input": tool_call_result["args"], "output": tool_message} + ) + + # Stream LLM response + for chunk in response_chain.stream(input=input): + yield OnChatModelStreamEvent(data={"chunk": chunk}) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/templates.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/templates.py new file mode 100644 index 0000000000..40a4f93b93 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/templates.py @@ -0,0 +1,41 @@ +from langchain_core.prompts import ( + ChatPromptTemplate, + MessagesPlaceholder, + PromptTemplate, +) + +template_docs = PromptTemplate.from_template( + """## Context provided: +{% for doc in docs%} + +{{ doc.page_content | safe }} + +{% endfor %} +""", + template_format="jinja2", +) + +inspect_conversation_template = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are an AI assistant tasked with analyzing the conversation " +and determining the best course of action.""", + ), + MessagesPlaceholder(variable_name="messages"), + ] +) + +rag_template = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are an AI assistant for question-answering tasks. + +Answer to the best of your ability using the context provided. +If you're unsure, it's better to acknowledge limitations than to speculate. +""", + ), + MessagesPlaceholder(variable_name="messages"), + ] +) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/vector_store.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/custom_rag_qa/vector_store.py new file mode 100644 index 0000000000..017d1383a1 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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/e2e-gen-ai-app-starter-kit/app/patterns/langgraph_dummy_agent/chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/langgraph_dummy_agent/chain.py new file mode 100644 index 0000000000..7a12f65b5a --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/patterns/langgraph_dummy_agent/chain.py @@ -0,0 +1,72 @@ +# 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-002", 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.""" + system_message = "You are a helpful AI assistant." + messages_with_system = [{"type": "system", "content": system_message}] + state[ + "messages" + ] + response = llm.invoke(messages_with_system, 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() diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/server.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/server.py new file mode 100644 index 0000000000..b68df946c8 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/server.py @@ -0,0 +1,115 @@ +# 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 +from typing import AsyncGenerator +import uuid + +# ruff: noqa: I001 +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 +from fastapi import FastAPI +from fastapi.responses import RedirectResponse, StreamingResponse +from google.cloud import logging as google_cloud_logging +from traceloop.sdk import Instruments, Traceloop + +## 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 = google_cloud_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/e2e-gen-ai-app-starter-kit/app/utils/__init__.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/input_types.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/input_types.py new file mode 100644 index 0000000000..49be86317c --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/input_types.py @@ -0,0 +1,56 @@ +# 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 Annotated, Any, List, Literal, Optional, Union + +from langchain_core.messages import AIMessage, HumanMessage, ToolMessage +from pydantic import BaseModel, Field + + +class InputChat(BaseModel): + """Represents the input for a chat session.""" + + messages: List[ + Annotated[ + Union[HumanMessage, AIMessage, ToolMessage], Field(discriminator="type") + ] + ] = 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] = "" + 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/e2e-gen-ai-app-starter-kit/app/utils/output_types.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/output_types.py new file mode 100644 index 0000000000..a40ea471ce --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/output_types.py @@ -0,0 +1,124 @@ +# 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, Literal + +from langchain_core.messages import AIMessage, AIMessageChunk, ToolMessage +from pydantic import BaseModel, Field +from traceloop.sdk import TracerWrapper +from traceloop.sdk.decorators import workflow + + +class BaseCustomChainEvent(BaseModel): + name: str = "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: ToolMessage + + +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) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/tracing.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/tracing.py new file mode 100644 index 0000000000..0f5ca84b80 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/app/utils/tracing.py @@ -0,0 +1,150 @@ +# 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 google_cloud_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[google_cloud_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 google_cloud_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 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/README.md new file mode 100644 index 0000000000..efdad5f2f2 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/README.md @@ -0,0 +1,116 @@ +# Deployment README.md + +This folder contains the infrastructure-as-code and CI/CD pipeline configurations for deploying a conversational Generative AI 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 Google Cloud settings. + + | Variable | Description | Required | + | ---------------------- | --------------------------------------------------------------- | :------: | + | prod_project_id | **Production** Google Cloud Project ID for resource deployment. | Yes | + | staging_project_id | **Staging** Google Cloud Project ID for resource deployment. | Yes | + | cicd_runner_project_id | Google Cloud Project ID where CI/CD pipelines will execute. | Yes | + | region | Google Cloud 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 repository 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 +``` + +### End-to-end Demo video + + + Watch the video + diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/cd/deploy-to-prod.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/cd/deploy-to-prod.yaml new file mode 100644 index 0000000000..896de2eba8 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/cd/staging.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/cd/staging.yaml new file mode 100644 index 0000000000..ff8eac0656 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/cd/staging.yaml @@ -0,0 +1,137 @@ +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 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/ci/pr_checks.yaml b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/ci/pr_checks.yaml new file mode 100644 index 0000000000..c018170ec2 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/apis.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/apis.tf new file mode 100644 index 0000000000..11e3d9f1df --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/apis.tf @@ -0,0 +1,49 @@ +locals { + cicd_services = [ + "cloudbuild.googleapis.com", + "discoveryengine.googleapis.com", + "aiplatform.googleapis.com", + "serviceusage.googleapis.com", + "bigquery.googleapis.com", + "cloudresourcemanager.googleapis.com", + "cloudtrace.googleapis.com" + ] + + shared_services = [ + "aiplatform.googleapis.com", + "run.googleapis.com", + "discoveryengine.googleapis.com", + "cloudresourcemanager.googleapis.com", + "iam.googleapis.com", + "bigquery.googleapis.com", + "serviceusage.googleapis.com", + "logging.googleapis.com", + "cloudtrace.googleapis.com" + ] + + projects = { + prod = var.prod_project_id + staging = var.staging_project_id + } + +} + +resource "google_project_service" "cicd_services" { + count = length(local.cicd_services) + project = var.cicd_runner_project_id + 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/e2e-gen-ai-app-starter-kit/deployment/terraform/artifact_registry.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/artifact_registry.tf new file mode 100644 index 0000000000..239b65e021 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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 Generative AI 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/e2e-gen-ai-app-starter-kit/deployment/terraform/build_triggers.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/build_triggers.tf new file mode 100644 index 0000000000..a0fc872c67 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/iam.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/iam.tf new file mode 100644 index 0000000000..a498653328 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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:${google_service_account.cloud_run_app_sa.email}" +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/log_sinks.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/log_sinks.tf new file mode 100644 index 0000000000..80fa73e42d --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/log_sinks.tf @@ -0,0 +1,49 @@ +resource "google_project_iam_member" "bigquery_data_editor" { + + project = var.dev_project_id + role = "roles/bigquery.dataEditor" + member = module.log_export_to_bigquery.writer_identity +} + + +module "log_export_to_bigquery" { + + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + + log_sink_name = var.telemetry_sink_name + parent_resource_type = "project" + parent_resource_id = var.dev_project_id + destination_uri = "bigquery.googleapis.com/projects/${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/${var.feedback_bigquery_dataset_id}" + filter = var.feedback_logs_filter + bigquery_options = { use_partitioned_tables = true } + unique_writer_identity = true +} + +resource "google_bigquery_dataset" "telemetry_logs_dataset" { + project = var.dev_project_id + dataset_id = var.telemetry_bigquery_dataset_id + friendly_name = var.telemetry_bigquery_dataset_id + location = var.region +} \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/service_accounts.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/service_accounts.tf new file mode 100644 index 0000000000..935bd815f5 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/service_accounts.tf @@ -0,0 +1,5 @@ +resource "google_service_account" "cloud_run_app_sa" { + account_id = var.cloud_run_app_sa_name + display_name = "Cloud Run Generative AI app SA" + project = var.dev_project_id +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/storage.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/storage.tf new file mode 100644 index 0000000000..20d7a1ecce --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/storage.tf @@ -0,0 +1,29 @@ +terraform { + required_version = ">= 1.0.0" + required_providers { + google = { + source = "hashicorp/google" + version = ">= 3.53.0, < 6.0.0" + } + } +} + +resource "google_storage_bucket" "logs_data_bucket" { + name = "${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/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/variables.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/variables.tf new file mode 100644 index 0000000000..eddb5d9f60 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/variables.tf @@ -0,0 +1,64 @@ +variable "dev_project_id" { + type = string + description = "**Dev** Google Cloud Project ID for resource deployment." +} + +variable "region" { + type = string + description = "Google Cloud 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/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/vars/env.tfvars b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/vars/env.tfvars new file mode 100644 index 0000000000..4e75cc2b36 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/dev/vars/env.tfvars @@ -0,0 +1,16 @@ +# Your Dev Google Cloud project id +dev_project_id = "your-dev-project-id" + +# The Google Cloud 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/e2e-gen-ai-app-starter-kit/deployment/terraform/iam.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/iam.tf new file mode 100644 index 0000000000..ddd3587294 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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:${resource.google_service_account.cicd_runner_sa.email}" + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/log_sinks.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/log_sinks.tf new file mode 100644 index 0000000000..676d68215c --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/log_sinks.tf @@ -0,0 +1,58 @@ +resource "google_project_iam_member" "bigquery_data_editor" { + for_each = local.project_ids + + project = each.value + role = "roles/bigquery.dataEditor" + member = module.log_export_to_bigquery[each.key].writer_identity +} + +module "log_export_to_bigquery" { + for_each = local.project_ids + + source = "terraform-google-modules/log-export/google" + version = "8.1.0" + + log_sink_name = var.telemetry_sink_name + parent_resource_type = "project" + parent_resource_id = each.value + destination_uri = "bigquery.googleapis.com/projects/${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/e2e-gen-ai-app-starter-kit/deployment/terraform/service_accounts.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/service_accounts.tf new file mode 100644 index 0000000000..d158071bab --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/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 Generative AI app SA" + project = each.value + depends_on = [resource.google_project_service.cicd_services, resource.google_project_service.shared_services] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/storage.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/storage.tf new file mode 100644 index 0000000000..a6f994cb98 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/storage.tf @@ -0,0 +1,37 @@ +terraform { + required_version = ">= 1.0.0" + required_providers { + google = { + source = "hashicorp/google" + version = ">= 3.53.0, < 6.0.0" + } + } +} + +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/e2e-gen-ai-app-starter-kit/deployment/terraform/variables.tf b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/variables.tf new file mode 100644 index 0000000000..57e7f05f63 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/variables.tf @@ -0,0 +1,125 @@ +variable "prod_project_id" { + type = string + description = "**Production** Google Cloud Project ID for resource deployment." +} + +variable "staging_project_id" { + type = string + description = "**Staging** Google Cloud Project ID for resource deployment." +} + +variable "cicd_runner_project_id" { + type = string + description = "Google Cloud Project ID where CI/CD pipelines will execute." +} + +variable "region" { + type = string + description = "Google Cloud 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"] +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/vars/env.tfvars b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/vars/env.tfvars new file mode 100644 index 0000000000..52679238c6 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/deployment/terraform/vars/env.tfvars @@ -0,0 +1,31 @@ +# Your Production Google Cloud project id +prod_project_id = "your-production-project-id" + +# Your Staging / Test Google Cloud project id +staging_project_id = "your-staging-project-id" + +# Your Google Cloud 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 Google Cloud 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 a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/images/chain_dev_cycle.png 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\"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7ZX50cNFOFBt" + }, + "source": [ + "# Getting Started - Template" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "84eed97da4c4" + }, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \"Google
Open in Colab\n", + " \n", + " 		\n", + " \n", + " \"Google
Open in Colab Enterprise\n", + " \n", + " 		\n", + " \n", + " \"Vertex
Open in Vertex AI Workbench\n", + " \n", + " 		\n", + " \n", + " \"GitHub
View on GitHub\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "usd0d_LiOFBt" + }, + "source": [ + "| | |\n", + "|-|-|\n", + "|Author(s) | [Elia Secchi](https://github.com/eliasecchig) |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MjDmmmDaOFBt" + }, + "source": [ + "## Overview\n", + "\n", + "This tutorial walks you through the process of developing and assessing a chain - a sequence of steps that power an AI application. \n", + "These operations may include interactions with language models, utilization of tools, or data preprocessing steps, aiming to solve a given use case e.g a chatbot that provides grounded information.\n", + "\n", + "You'll learn how to:\n", + "\n", + "1. Build chains using three different approaches:\n", + " - [LangChain Expression Language (LCEL)](https://python.langchain.com/docs/expression_language/)\n", + " - [LangGraph](https://python.langchain.com/docs/langgraph/)\n", + " - A custom Python implementation. This is to enable implementation with other SDKs ( e.g [Vertex AI SDK](https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk ), [LlamaIndex](https://www.llamaindex.ai/)) and to allow granular control on the sequence of steps in the chain\n", + " \n", + "2. Evaluate the performance of your chains using [Vertex AI Evaluation](https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview)\n", + "\n", + "Finally, the tutorial discusses next steps for deploying your chain in a production application\n", + "\n", + "By the end of this tutorial, you'll have a solid foundation for developing and refining your own Generative AI chains." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w-OcPSC8_FUX" + }, + "source": [ + "## Get Started" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c0a13ca7427f" + }, + "source": [ + "### Install required packages using Poetry (Recommended)\n", + "\n", + "This template uses [Poetry](https://python-poetry.org/) as tool to manage project dependencies. \n", + "Poetry makes it easy to install and keep track of the packages your project needs.\n", + "\n", + "To run this notebook with Poetry, follow these steps:\n", + "1. Make sure Poetry is installed. See the [relative guide for installation](https://python-poetry.org/docs/#installation).\n", + "\n", + "2. Make sure that dependencies are installed. From your command line:\n", + " ```bash\n", + " poetry install --with streamlit,jupyter\n", + " ```\n", + "\n", + "3. Run Jupyter:\n", + " ```bash\n", + " poetry run jupyter\n", + " ```\n", + " \n", + "4. Open this notebook in the Jupyter interface." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-7Jso8-FO4N8" + }, + "source": [ + "### (Alternative) Install Vertex AI SDK and other required packages " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tUat7NRq5JDC" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade nest_asyncio\n", + "%pip install --upgrade --user --quiet langchain-core langchain-google-vertexai langchain-google-community langchain langgraph\n", + "%pip install --upgrade --user --quiet \"google-cloud-aiplatform[rapid_evaluation]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R5Xep4W9lq-Z" + }, + "source": [ + "### Restart runtime\n", + "\n", + "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which restarts the current kernel.\n", + "\n", + "The restart might take a minute or longer. After it's restarted, continue to the next step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XRvKdaPDTznN" + }, + "outputs": [], + "source": [ + "import IPython\n", + "\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SbmM4z7FOBpM" + }, + "source": [ + "
\n", + "⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dmWOrTJ3gx13" + }, + "source": [ + "### Authenticate your notebook environment (Colab only)\n", + "\n", + "If you're running this notebook on Google Colab, run the cell below to authenticate your environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NyKGtVQjgx13" + }, + "outputs": [], + "source": [ + "# import sys\n", + "\n", + "# if \"google.colab\" in sys.modules:\n", + "# from google.colab import auth\n", + "\n", + "# auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DF4l8DTdWgPY" + }, + "source": [ + "### Set Google Cloud project information and initialize Vertex AI SDK\n", + "\n", + "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n", + "\n", + "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Nqwi-5ufWp_B" + }, + "outputs": [], + "source": [ + "# Use the environment variable if the user doesn't provide Project ID.\n", + "import os\n", + "\n", + "import vertexai\n", + "\n", + "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\", isTemplate: true}\n", + "if PROJECT_ID == \"[your-project-id]\":\n", + " PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n", + "\n", + "LOCATION = os.environ.get(\"GOOGLE_CLOUD_REGION\", \"us-central1\")\n", + "\n", + "vertexai.init(project=PROJECT_ID, location=LOCATION)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dvhI92xhQTzk" + }, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "869d543465ac" + }, + "outputs": [], + "source": [ + "# Add the parent directory to the Python path. This allows importing modules from the parent directory\n", + "import sys\n", + "\n", + "sys.path.append(\"../\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "146b41115577" + }, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "import yaml\n", + "from typing import Any, Dict, Iterator, Literal, List\n", + "\n", + "from langchain_core.messages import ToolMessage\n", + "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", + "from langchain_core.runnables import RunnableConfig\n", + "from langchain_core.tools import tool\n", + "from langchain_google_community.vertex_rank import VertexAIRank\n", + "from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings\n", + "from langgraph.graph import END, MessagesState, StateGraph\n", + "from langgraph.prebuilt import ToolNode\n", + "from langchain.schema import Document\n", + "from google.cloud import aiplatform\n", + "from vertexai.evaluation import CustomMetric, EvalTask\n", + "\n", + "from app.eval.utils import batch_generate_messages, generate_multiturn_history\n", + "from app.patterns.custom_rag_qa.templates import (\n", + " inspect_conversation_template,\n", + " rag_template,\n", + " template_docs,\n", + ")\n", + "from app.patterns.custom_rag_qa.vector_store import get_vector_store\n", + "from app.utils.output_types import OnChatModelStreamEvent, OnToolEndEvent, custom_chain" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "675dff1826d4" + }, + "source": [ + "## Chain Interface\n", + "\n", + "This section outlines a possible interface for the chain, which, if implemented, ensures compatibility with the FastAPI server application included in the template. However, it's important to note that you have the flexibility to explore and implement alternative interfaces that suit their specific needs and requirements.\n", + "\n", + "\n", + "### Input Interface\n", + "\n", + "The chain must provide an `astream_events` method that accepts a dictionary with a \"messages\" key.\n", + "The \"messages\" value should be a list of LangChain [HumanMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.human.HumanMessage.html), [AIMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessage.html) objects and [ToolMessage](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolMessage.html).\n", + "\n", + "For example a possible input might be:\n", + "\n", + "```python\n", + "{\n", + " \"messages\": [\n", + " HumanMessage(\"first\"),\n", + " AIMessage(\"a response\"),\n", + " HumanMessage(\"a follow up\")\n", + " ]\n", + "}\n", + "```\n", + "\n", + "Alternatively you can use the shortened form:\n", + "\n", + "```python\n", + "{\n", + " \"messages\": [\n", + " (\"user\", \"first\"),\n", + " (\"ai\", \"a response\"),\n", + " (\"user\", \"a follow up\")\n", + " ]\n", + "}\n", + "```\n", + "\n", + "### Output Interface\n", + "\n", + "All chains use the [LangChain Stream Events (v2) API](https://python.langchain.com/docs/how_to/streaming/#using-stream-events). This API supports various use cases (simple chains, RAG, Agents). This API emits asynchronous events that can be used to stream the chain's output.\n", + "\n", + "LangChain chains (LCEL, LangGraph) automatically implement the `astream_events` API. \n", + "\n", + "We provide examples of emitting `astream_events`-compatible events with custom Python code, allowing implementation with other SDKs (e.g., Vertex AI, LLamaIndex).\n", + "\n", + "### Customizing I/O Interfaces\n", + "\n", + "To modify the Input/Output interface, update `app/server.py` and related unit and integration tests.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "865ba0268d3b" + }, + "source": [ + "## Events supported\n", + "\n", + "The following list defines the events that are captured and supported by the Streamlit frontend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4ece481555a2" + }, + "outputs": [], + "source": [ + "SUPPORTED_EVENTS = [\n", + " \"on_tool_start\",\n", + " \"on_tool_end\",\n", + " \"on_retriever_start\",\n", + " \"on_retriever_end\",\n", + " \"on_chat_model_stream\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "874120e6a4d2" + }, + "source": [ + "### Define the LLM\n", + "We set up the Large Language Model (LLM) for our conversational bot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0d5c52272898" + }, + "outputs": [], + "source": [ + "llm = ChatVertexAI(model_name=\"gemini-1.5-flash-002\", temperature=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "abc296e9da88" + }, + "source": [ + "### Leverage LangChain LCEL\n", + "\n", + "LangChain Expression Language (LCEL) provides a declarative approach to composing chains seamlessly. Key benefits include:\n", + "\n", + "1. Rapid prototyping to production deployment without code changes\n", + "2. Scalability from simple \"prompt + LLM\" chains to complex, multi-step workflows\n", + "3. Enhanced readability and maintainability of chain logic\n", + "\n", + "For comprehensive guidance on LCEL implementation, refer to the [official documentation](https://python.langchain.com/docs/expression_language/get_started).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "25d185f29f42" + }, + "outputs": [], + "source": [ + "template = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a knowledgeable culinary assistant specializing in providing\"\n", + " \"detailed cooking recipes. Your responses should be informative, engaging, \"\n", + " \"and tailored to the user's specific requests. Include ingredients, \"\n", + " \"step-by-step instructions, cooking times, and any helpful tips or \"\n", + " \"variations. If asked about dietary restrictions or substitutions, offer \"\n", + " \"appropriate alternatives.\",\n", + " ),\n", + " MessagesPlaceholder(variable_name=\"messages\"),\n", + " ]\n", + ")\n", + "\n", + "chain = template | llm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "216f187e66d7" + }, + "source": [ + "Let's test the chain with a dummy question:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "267a75f53b45" + }, + "outputs": [], + "source": [ + "input_message = {\"messages\": [(\"human\", \"Can you provide me a Lasagne recipe?\")]}\n", + "\n", + "async for event in chain.astream_events(input=input_message, version=\"v2\"):\n", + " if event[\"event\"] in SUPPORTED_EVENTS:\n", + " print(event[\"data\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b262558c2375" + }, + "source": [ + "This methodology is used for the chain defined in the [`app/chain.py`](../app/chain.py) file.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1fa3c684b527" + }, + "source": [ + "We can also leverage the `invoke` method for synchronous invocation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5fc2bfec1d5b" + }, + "outputs": [], + "source": [ + "response = chain.invoke(input=input_message)\n", + "print(response.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "16a9fd5fdf2f" + }, + "source": [ + "### Use LangGraph\n", + "\n", + "LangGraph is a framework for building stateful, multi-actor applications with Large Language Models (LLMs). \n", + "It extends the LangChain library, allowing you to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "28b4ad81f8b7" + }, + "outputs": [], + "source": [ + "# 1. Define tools\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Simulates a web search. Use it get information on weather. E.g what is the weather like in a region\"\"\"\n", + " if \"sf\" in query.lower() or \"san francisco\" in query.lower():\n", + " return \"It's 60 degrees and foggy.\"\n", + " return \"It's 90 degrees and sunny.\"\n", + "\n", + "\n", + "tools = [search]\n", + "\n", + "# 2. Set up the language model\n", + "llm = llm.bind_tools(tools)\n", + "\n", + "\n", + "# 3. Define workflow components\n", + "def should_continue(state: MessagesState) -> Literal[\"tools\", END]:\n", + " \"\"\"Determines whether to use tools or end the conversation.\"\"\"\n", + " last_message = state[\"messages\"][-1]\n", + " return \"tools\" if last_message.tool_calls else END\n", + "\n", + "\n", + "async def call_model(state: MessagesState, config: RunnableConfig):\n", + " \"\"\"Calls the language model and returns the response.\"\"\"\n", + " response = llm.invoke(state[\"messages\"], config)\n", + " return {\"messages\": response}\n", + "\n", + "\n", + "# 4. Create the workflow graph\n", + "workflow = StateGraph(MessagesState)\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"tools\", ToolNode(tools))\n", + "workflow.set_entry_point(\"agent\")\n", + "\n", + "# 5. Define graph edges\n", + "workflow.add_conditional_edges(\"agent\", should_continue)\n", + "workflow.add_edge(\"tools\", \"agent\")\n", + "\n", + "# 6. Compile the workflow\n", + "chain = workflow.compile()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bd27c49717e8" + }, + "source": [ + "Let's test the new chain with a dummy question:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a33402d68f7b" + }, + "outputs": [], + "source": [ + "input_message = {\"messages\": [(\"human\", \"What is the weather like in NY?\")]}\n", + "\n", + "async for event in chain.astream_events(input=input_message, version=\"v2\"):\n", + " if event[\"event\"] in SUPPORTED_EVENTS:\n", + " print(event[\"data\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2cf0ad148bf8" + }, + "source": [ + "This methodology is used for the chain defined in the [`app/patterns/langgraph_dummy_agent/chain.py`](../app/patterns/langgraph_dummy_agent/chain.py) file.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ae7dad0966b" + }, + "source": [ + "### Use custom python code\n", + "\n", + "You can also use pure python code to orchestrate the different steps of your chain and emit `astream_events` [API compatible events](https://python.langchain.com/docs/how_to/streaming/#using-stream-events). \n", + "\n", + "This offers full flexibility in how the different steps of a chain are orchestrated and allows you to include other SDK frameworks such as [Vertex AI SDK](https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk ), [LlamaIndex](https://www.llamaindex.ai/).\n", + "\n", + "We demonstrate this third methodology by implementing a RAG chain. The function `get_vector_store` provides a brute force Vector store (scikit-learn) initialized with data obtained from the [practictioners guide for MLOps](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = ChatVertexAI(model_name=\"gemini-1.5-flash-002\", temperature=0)\n", + "embedding = VertexAIEmbeddings(model_name=\"text-embedding-004\")\n", + "\n", + "\n", + "vector_store = get_vector_store(embedding=embedding)\n", + "retriever = vector_store.as_retriever(search_kwargs={\"k\": 20})\n", + "compressor = VertexAIRank(\n", + " project_id=PROJECT_ID,\n", + " location_id=\"global\",\n", + " ranking_config=\"default_ranking_config\",\n", + " title_field=\"id\",\n", + " top_n=5,\n", + ")\n", + "\n", + "\n", + "@tool\n", + "def retrieve_docs(query: str) -> List[Document]:\n", + " \"\"\"\n", + " Useful for retrieving relevant documents based on a query.\n", + " Use this when you need additional information to answer a question.\n", + "\n", + " Args:\n", + " query (str): The user's question or search query.\n", + "\n", + " Returns:\n", + " List[Document]: A list of the top-ranked Document objects, limited to TOP_K (5) results.\n", + " \"\"\"\n", + " retrieved_docs = retriever.invoke(query)\n", + " ranked_docs = compressor.compress_documents(documents=retrieved_docs, query=query)\n", + " return ranked_docs\n", + "\n", + "\n", + "@tool\n", + "def should_continue() -> None:\n", + " \"\"\"\n", + " Use this tool if you determine that you have enough context to respond to the questions of the user.\n", + " \"\"\"\n", + " return None\n", + "\n", + "\n", + "# Set up conversation inspector\n", + "inspect_conversation = inspect_conversation_template | llm.bind_tools(\n", + " [retrieve_docs, should_continue], tool_choice=\"any\"\n", + ")\n", + "\n", + "# Set up response chain\n", + "response_chain = rag_template | llm\n", + "\n", + "\n", + "@custom_chain\n", + "def chain(\n", + " input: Dict[str, Any], **kwargs: Any\n", + ") -> Iterator[OnToolEndEvent | OnChatModelStreamEvent]:\n", + " \"\"\"\n", + " Implement a RAG QA chain with tool calls.\n", + "\n", + " This function is decorated with `custom_chain` to offer LangChain compatible\n", + " astream_events, support for synchronous invocation through the `invoke` method,\n", + " and OpenTelemetry tracing.\n", + " \"\"\"\n", + " # Inspect conversation and determine next action\n", + " inspection_result = inspect_conversation.invoke(input)\n", + " tool_call_result = inspection_result.tool_calls[0]\n", + "\n", + " # Execute the appropriate tool based on the inspection result\n", + " if tool_call_result[\"name\"] == \"retrieve_docs\":\n", + " # Retrieve relevant documents\n", + " docs = retrieve_docs.invoke(tool_call_result[\"args\"])\n", + " # Format the retrieved documents\n", + " formatted_docs = template_docs.format(docs=docs)\n", + " # Create a ToolMessage with the formatted documents\n", + " tool_message = ToolMessage(\n", + " tool_call_id=tool_call_result[\"name\"],\n", + " name=tool_call_result[\"name\"],\n", + " content=formatted_docs,\n", + " artifact=docs,\n", + " )\n", + " else:\n", + " # If no documents need to be retrieved, continue with the conversation\n", + " tool_message = should_continue.invoke(tool_call_result)\n", + "\n", + " # Update input messages with new information\n", + " input[\"messages\"] = input[\"messages\"] + [inspection_result, tool_message]\n", + "\n", + " # Yield tool results metadata\n", + " yield OnToolEndEvent(\n", + " data={\"input\": tool_call_result[\"args\"], \"output\": tool_message}\n", + " )\n", + "\n", + " # Stream LLM response\n", + " for chunk in response_chain.stream(input=input):\n", + " yield OnChatModelStreamEvent(data={\"chunk\": chunk})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2d59f2641aaf" + }, + "source": [ + "The `@custom_chain` decorator defined in `app/utils/output_types.py`:\n", + "- Enables compatibility with the `astream_events` LangChain API interface by offering a `chain.astream_events` method.\n", + "- Provides an `invoke` method for synchronous invocation. This method can be utilized for evaluation purposes.\n", + "- Adds OpenTelemetry tracing functionality.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1b0d16359361" + }, + "source": [ + "This methodology is used for the chain defined in `app/patterns/custom_rag_qa/chain.py` file.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "82f22f13fde6" + }, + "source": [ + "Let's test the custom chain we just created. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7a1bcac73ff5" + }, + "outputs": [], + "source": [ + "input_message = {\"messages\": [(\"human\", \"What is MLOps?\")]}\n", + "\n", + "async for event in chain.astream_events(input=input_message, version=\"v2\"):\n", + " if event[\"event\"] in SUPPORTED_EVENTS:\n", + " print(event[\"data\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8730b4b6bbff" + }, + "source": [ + "## Evaluation\n", + "\n", + "Evaluation is the activity of assessing the quality of the model's outputs, to gauge its understanding and success in fulfilling the prompt's instructions.\n", + "\n", + "In the context of Generative AI, evaluation extends beyond the evaluation of the model's outputs to include the evaluation of the chain's outputs and in some cases the evaluation of the intermediate steps (for example, the evaluation of the retriever's outputs).\n", + "\n", + "### Vertex AI Evaluation\n", + "To evaluate the chain's outputs, we'll utilize [Vertex AI Evaluation](https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview) to assess our AI application's performance. \n", + "Vertex AI Evaluation streamlines the evaluation process for generative AI by offering three key features:\n", + "\n", + "- [Pre-built Metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval): It provides a library of ready-to-use metrics for common evaluation tasks, saving you time and effort in defining your own. These metrics cover a range of areas, simplifying the assessment of different aspects of your model's performance.\n", + " \n", + "- [Custom Metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval): Beyond pre-built options, Vertex AI Evaluation allows you to define and implement custom metrics tailored to your specific needs and application requirements. \n", + " \n", + "- Strong Integration with [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments): Vertex AI Evaluation seamlessly integrates with Vertex AI Experiments, creating a unified workflow for tracking experiments and managing evaluation results.\n", + "\n", + "For a comprehensive list of samples on Vertex AI Evaluation, visit the [official documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-examples)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e214b9a02547" + }, + "source": [ + "Let's start by defining again a simple chain:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "086298b785fb" + }, + "outputs": [], + "source": [ + "template = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a knowledgeable culinary assistant specializing in providing\"\n", + " \"detailed cooking recipes. Your responses should be informative, engaging, \"\n", + " \"and tailored to the user's specific requests. Include ingredients, \"\n", + " \"step-by-step instructions, cooking times, and any helpful tips or \"\n", + " \"variations. If asked about dietary restrictions or substitutions, offer \"\n", + " \"appropriate alternatives.\",\n", + " ),\n", + " MessagesPlaceholder(variable_name=\"messages\"),\n", + " ]\n", + ")\n", + "\n", + "chain = template | llm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "284ec00b23c7" + }, + "source": [ + "We then import the ground truth data we will use for evaluation. Data is stored in [`app/eval/data/chats.yaml`](../app/eval/data/chats.yaml)\n", + "Note: You might need to adjust the path depending on where your Jupyter kernel was initialized.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f7c05ab676d8" + }, + "outputs": [], + "source": [ + "y = yaml.safe_load(open(\"../app/eval/data/chats.yaml\"))\n", + "df = pd.DataFrame(y)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "432ce6674c19" + }, + "source": [ + "We leverage the helper functions [`generate_multiturn_history`](../app/eval/utils.py) and [`batch_generate_messages`](../app/eval/utils.py) to prepare the data for evaluation and to generate the responses from the chain.\n", + "\n", + "You can see below the documentation for the two functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3a6899faf8b3" + }, + "outputs": [], + "source": [ + "help(generate_multiturn_history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6afe1b67475f" + }, + "outputs": [], + "source": [ + "help(batch_generate_messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "b5e1411cf0f4" + }, + "outputs": [], + "source": [ + "df = generate_multiturn_history(df)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9ff1aaed7eb0" + }, + "outputs": [], + "source": [ + "scored_data = batch_generate_messages(df, chain)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "933c797dc79e" + }, + "source": [ + "We extract the user message and the reference (ground truth) message from dataframe so that we can use them for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5e5ba1acd013" + }, + "outputs": [], + "source": [ + "scored_data[\"user\"] = scored_data[\"human_message\"].apply(lambda x: x[\"content\"])\n", + "scored_data[\"reference\"] = scored_data[\"ai_message\"].apply(lambda x: x[\"content\"])\n", + "scored_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dc41cc55703e" + }, + "source": [ + "#### Define a CustomMetric using Gemini model\n", + "\n", + "Define a customized Gemini model-based metric function, with explanations for the score. The registered custom metrics are computed on the client side, without using online evaluation service APIs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "51c0ffb825e1" + }, + "outputs": [], + "source": [ + "evaluator_llm = ChatVertexAI(\n", + " model_name=\"gemini-1.5-flash-001\",\n", + " temperature=0,\n", + " response_mime_type=\"application/json\",\n", + ")\n", + "\n", + "\n", + "def custom_faithfulness(instance):\n", + " prompt = f\"\"\"You are examining written text content. Here is the text:\n", + "************\n", + "Written content: {instance[\"response\"]}\n", + "************\n", + "Original source data: {instance[\"reference\"]}\n", + "\n", + "Examine the text and determine whether the text is faithful or not.\n", + "Faithfulness refers to how accurately a generated summary reflects the essential information and key concepts present in the original source document.\n", + "A faithful summary stays true to the facts and meaning of the source text, without introducing distortions, hallucinations, or information that wasn't originally there.\n", + "\n", + "Your response must be an explanation of your thinking along with single integer number on a scale of 0-5, 0\n", + "the least faithful and 5 being the most faithful.\n", + "\n", + "Produce results in JSON\n", + "\n", + "Expected format:\n", + "\n", + "```json\n", + "{{\n", + " \"explanation\": \"< your explanation>\",\n", + " \"custom_faithfulness\": \n", + "}}\n", + "```\n", + "\"\"\"\n", + "\n", + " result = evaluator_llm.invoke([(\"human\", prompt)])\n", + " result = json.loads(result.content)\n", + " return result\n", + "\n", + "\n", + "# Register Custom Metric\n", + "custom_faithfulness_metric = CustomMetric(\n", + " name=\"custom_faithfulness\",\n", + " metric_function=custom_faithfulness,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "234f1b868cb9" + }, + "outputs": [], + "source": [ + "experiment_name = \"template-langchain-eval\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f5b15279f9d2" + }, + "source": [ + "We are now ready to run the evaluation. We will use different metrics, combining the custom metric we defined above with some pre-built metrics.\n", + "\n", + "Results of the evaluation will be automatically tagged into the experiment_name we define.\n", + "\n", + "You can click `View Experiment`, to see the experiment in Google Cloud Console." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4d3faf6014f5" + }, + "outputs": [], + "source": [ + "metrics = [\"fluency\", \"safety\", custom_faithfulness_metric]\n", + "\n", + "eval_task = EvalTask(\n", + " dataset=scored_data,\n", + " metrics=metrics,\n", + " experiment=experiment_name,\n", + " metric_column_mapping={\"prompt\": \"user\"},\n", + ")\n", + "eval_result = eval_task.evaluate()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c51deffd590c" + }, + "source": [ + "Once an eval result is produced, we are able to display summary metrics:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KheOvIvtiRlz" + }, + "outputs": [], + "source": [ + "eval_result.summary_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JcALGGlwu0p_" + }, + "source": [ + "We are also able to display a pandas dataframe containing a detailed summary of how our eval dataset performed and relative granular metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9zJ686YYiWJC" + }, + "outputs": [], + "source": [ + "eval_result.metrics_table" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "378025ea68d0" + }, + "source": [ + "## Next Steps\n", + "\n", + "Congratulations on completing the getting started tutorial! You've learned different methodologies to build a chain and how to evaluate it. \n", + "Let's explore the next steps in your journey:\n", + "\n", + "### 1. Prepare for Production\n", + "\n", + "Once you're satisfied with your chain's evaluation results:\n", + "\n", + "1. Write your chain into the [`app/chain.py` file](../app/chain.py).\n", + "2. Remove the `patterns` folder and its associated tests (these are for demonstration only).\n", + "\n", + "### 2. Local Testing\n", + "\n", + "Test your chain using the playground:\n", + "\n", + "```bash\n", + "make playground\n", + "```\n", + "\n", + "This launches af feature-rich playground, including chat curation, user feedback collection, multimodal input, and more!\n", + "\n", + "\n", + "### 3. Production Deployment\n", + "\n", + "Once you are satisfied with the results, you can setup your CI/CD pipelines to deploy your chain to production.\n", + "\n", + "Please refer to the [deployment guide](../deployment/README.md) for more information on how to do that." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7bf6a5b0def0" + }, + "source": [ + "## Cleaning up\n", + "\n", + "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", + "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", + "\n", + "Otherwise, you can delete the individual resources you created in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0236a4c1471d" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Delete Experiments\n", + "delete_experiments = True\n", + "if delete_experiments or os.getenv(\"IS_TESTING\"):\n", + " experiments_list = aiplatform.Experiment.list()\n", + " for experiment in experiments_list:\n", + " experiment.delete()" + ] + } + ], + "metadata": { + "colab": { + "name": "getting_started.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/poetry.lock b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/poetry.lock new file mode 100644 index 0000000000..d9f9308234 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/poetry.lock @@ -0,0 +1,7684 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by 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["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.10" +content-hash = "b4df90aef3b9d0f8434f4e7de24884c71c2a3414a5ac2da36d1d699640730bdb" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/pyproject.toml b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/pyproject.toml new file mode 100644 index 0000000000..b864565d37 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/pyproject.toml @@ -0,0 +1,101 @@ +[tool.poetry] +name = "starter-app-sample" +version = "0.1.0" +description = "" +authors = ["Your Name "] +packages = [ + { include = "app" }, +] + +[tool.poetry.dependencies] +python = "^3.10" +traceloop-sdk = "^0.30.1" +langchain-google-vertexai = "^2.0.0" +opentelemetry-exporter-gcp-trace = "^1.6.0" +opentelemetry-sdk = "^1.25.0" +google-cloud-logging = "^3.10.0" +langchain = "^0.3.0" +google-cloud-aiplatform = {extras = ["rapid-evaluation"], version = "^1.59.0"} +scikit-learn = "^1.5.0" +fastapi = "0.110.3" +langchain-google-community = {extras = ["vertexaisearch"], version = "^2.0.0"} +pypdf = "^4.3.1" +grpcio = "1.64.1" +langgraph = "^0.2.21" +uvicorn = {extras = ["standard"], version = "^0.30.5"} +immutabledict = "^4.2.0" +types-pyyaml = "^6.0.12.20240917" +types-requests = "^2.32.0.20240914" +langchain-core = "^0.3.9" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.3.2" +pytest-asyncio = "^0.23.8" +nest-asyncio = "^1.6.0" + + +[tool.poetry.group.streamlit] +optional = true + +[tool.poetry.group.streamlit.dependencies] +streamlit = "^1.35.0" +streamlit-extras = "^0.4.3" +extra-streamlit-components = "^0.1.71" +streamlit-feedback = "^0.1.3" + + +[tool.poetry.group.jupyter] +optional = true + +[tool.poetry.group.jupyter.dependencies] +jupyter = "^1.0.0" + + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" +mypy = "^1" +codespell = "^2.2.0" +black = "^24.8.0" + + +[tool.ruff.lint] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] +[tool.ruff] +line-length = 105 + +[tool.mypy] +disallow_untyped_calls = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +no_implicit_optional = true +check_untyped_defs = true +disallow_subclassing_any = true +warn_incomplete_stub = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_unreachable = true +follow_imports = "silent" +ignore_missing_imports = true +explicit_package_bases = true +disable_error_code = ["misc", "no-untyped-call", "no-any-return"] + + + +[tool.codespell] +ignore-words-list = "rouge" +skip = "./locust_env/*,poetry.lock" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +pythonpath = "." \ No newline at end of file diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/side_bar.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/side_bar.py new file mode 100644 index 0000000000..3b934e9da7 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/side_bar.py @@ -0,0 +1,161 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import uuid +from typing import Any + +from utils.chat_utils import save_chat +from utils.multimodal_utils import ( + HELP_GCS_CHECKBOX, + HELP_MESSAGE_MULTIMODALITY, + upload_files_to_gcs, +) + +EMPTY_CHAT_NAME = "Empty chat" +NUM_CHAT_IN_RECENT = 3 +DEFAULT_BASE_URL = "http://localhost:8000/" + + +class SideBar: + def __init__(self, st: Any) -> None: + self.st = st + + def init_side_bar(self) -> None: + with self.st.sidebar: + self.url_input_field = self.st.text_input( + label="Service URL", + value=os.environ.get("SERVICE_URL", DEFAULT_BASE_URL), + ) + self.should_authenticate_request = self.st.checkbox( + label="Authenticate request", + value=False, + help="If checked, any request to the server will contain an" + "Identity token to allow authentication. " + "See the Cloud Run documentation to know more about authentication:" + "https://cloud.google.com/run/docs/authenticating/service-to-service", + ) + col1, col2, col3 = self.st.columns(3) + with col1: + if self.st.button("+ New chat"): + if ( + len( + self.st.session_state.user_chats[ + self.st.session_state["session_id"] + ]["messages"] + ) + > 0 + ): + self.st.session_state.run_id = None + + self.st.session_state["session_id"] = str(uuid.uuid4()) + self.st.session_state.session_db.get_session( + session_id=self.st.session_state["session_id"], + ) + self.st.session_state.user_chats[ + self.st.session_state["session_id"] + ] = { + "title": EMPTY_CHAT_NAME, + "messages": [], + } + + with col2: + if self.st.button("Delete chat"): + self.st.session_state.run_id = None + self.st.session_state.session_db.clear() + self.st.session_state.user_chats.pop( + self.st.session_state["session_id"] + ) + if len(self.st.session_state.user_chats) > 0: + chat_id = list(self.st.session_state.user_chats.keys())[0] + self.st.session_state["session_id"] = chat_id + self.st.session_state.session_db.get_session( + session_id=self.st.session_state["session_id"], + ) + else: + self.st.session_state["session_id"] = str(uuid.uuid4()) + self.st.session_state.user_chats[ + self.st.session_state["session_id"] + ] = { + "title": EMPTY_CHAT_NAME, + "messages": [], + } + with col3: + if self.st.button("Save chat"): + save_chat(self.st) + + self.st.subheader("Recent") # Style the heading + + all_chats = list(reversed(self.st.session_state.user_chats.items())) + for chat_id, chat in all_chats[:NUM_CHAT_IN_RECENT]: + if self.st.button(chat["title"], key=chat_id): + self.st.session_state.run_id = None + self.st.session_state["session_id"] = chat_id + self.st.session_state.session_db.get_session( + session_id=self.st.session_state["session_id"], + ) + + with self.st.expander("Other chats"): + for chat_id, chat in all_chats[NUM_CHAT_IN_RECENT:]: + if self.st.button(chat["title"], key=chat_id): + self.st.session_state.run_id = None + self.st.session_state["session_id"] = chat_id + self.st.session_state.session_db.get_session( + session_id=self.st.session_state["session_id"], + ) + + self.st.divider() + self.st.header("Upload files from local") + bucket_name = self.st.text_input( + label="GCS Bucket for upload", + value=os.environ.get("BUCKET_NAME", "gs://your-bucket-name"), + ) + if "checkbox_state" not in self.st.session_state: + self.st.session_state.checkbox_state = True + + self.st.session_state.checkbox_state = self.st.checkbox( + "Upload to GCS first (suggested)", value=False, help=HELP_GCS_CHECKBOX + ) + + self.uploaded_files = self.st.file_uploader( + label="Send files from local", + accept_multiple_files=True, + key=f"uploader_images_{self.st.session_state.uploader_key}", + type=[ + "png", + "jpg", + "jpeg", + "txt", + "docx", + "pdf", + "rtf", + "csv", + "tsv", + "xlsx", + ], + ) + if self.uploaded_files and self.st.session_state.checkbox_state: + upload_files_to_gcs(self.st, bucket_name, self.uploaded_files) + + self.st.divider() + + self.st.header("Upload files from GCS") + self.gcs_uris = self.st.text_area( + "GCS uris (comma-separated)", + value=self.st.session_state["gcs_uris_to_be_sent"], + key=f"upload_text_area_{self.st.session_state.uploader_key}", + help=HELP_MESSAGE_MULTIMODALITY, + ) + + self.st.caption(f"Note: {HELP_MESSAGE_MULTIMODALITY}") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/streamlit_app.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/streamlit_app.py new file mode 100644 index 0000000000..a469a60395 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/streamlit_app.py @@ -0,0 +1,240 @@ +# 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 uuid +from functools import partial +from typing import Any, Dict, List + +from langchain_core.messages import HumanMessage +from side_bar import SideBar +from streamlit_feedback import streamlit_feedback +from style.app_markdown import markdown_str +from utils.local_chat_history import LocalChatMessageHistory +from utils.message_editing import MessageEditing +from utils.multimodal_utils import format_content, get_parts_from_files +from utils.stream_handler import Client, StreamHandler, get_chain_response + +import streamlit as st + +USER = "my_user" +EMPTY_CHAT_NAME = "Empty chat" + + +def setup_page() -> None: + st.set_page_config( + page_title="Playground", + layout="wide", + initial_sidebar_state="auto", + menu_items=None, + ) + st.title("Playground") + st.markdown(markdown_str, unsafe_allow_html=True) + + +def initialize_session_state() -> None: + if "user_chats" not in st.session_state: + st.session_state["session_id"] = str(uuid.uuid4()) + st.session_state.uploader_key = 0 + st.session_state.run_id = None + st.session_state.user_id = USER + st.session_state["gcs_uris_to_be_sent"] = "" + st.session_state.modified_prompt = None + st.session_state.session_db = LocalChatMessageHistory( + session_id=st.session_state["session_id"], + user_id=st.session_state["user_id"], + ) + st.session_state.user_chats = ( + st.session_state.session_db.get_all_conversations() + ) + st.session_state.user_chats[st.session_state["session_id"]] = { + "title": EMPTY_CHAT_NAME, + "messages": [], + } + + +def display_messages() -> None: + messages = st.session_state.user_chats[st.session_state["session_id"]]["messages"] + tool_call_input = None + for i, message in enumerate(messages): + if message["type"] in ["ai", "human"] and message["content"]: + display_chat_message(message, i) + elif "tool_calls" in message and message["tool_calls"]: + tool_call_input = handle_tool_call(message) + elif message["type"] == "tool" and tool_call_input is not None: + display_tool_output(tool_call_input, message) + tool_call_input = None + else: + st.error(f"Unexpected message type: {message['type']}") + st.write("Full messages list:", messages) + raise ValueError(f"Unexpected message type: {message['type']}") + + +def display_chat_message(message: Dict[str, Any], index: int) -> None: + chat_message = st.chat_message(message["type"]) + with chat_message: + st.markdown(format_content(message["content"]), unsafe_allow_html=True) + col1, col2, col3 = st.columns([2, 2, 94]) + display_message_buttons(message, index, col1, col2, col3) + + +def display_message_buttons( + message: Dict[str, Any], index: int, col1: Any, col2: Any, col3: Any +) -> None: + edit_button = f"{index}_edit" + refresh_button = f"{index}_refresh" + delete_button = f"{index}_delete" + content = ( + message["content"] + if isinstance(message["content"], str) + else message["content"][-1]["text"] + ) + + with col1: + st.button(label="✎", key=edit_button, type="primary") + if message["type"] == "human": + with col2: + st.button( + label="⟳", + key=refresh_button, + type="primary", + on_click=partial(MessageEditing.refresh_message, st, index, content), + ) + with col3: + st.button( + label="X", + key=delete_button, + type="primary", + on_click=partial(MessageEditing.delete_message, st, index), + ) + + if st.session_state[edit_button]: + st.text_area( + "Edit your message:", + value=content, + key=f"edit_box_{index}", + on_change=partial(MessageEditing.edit_message, st, index, message["type"]), + ) + + +def handle_tool_call(message: Dict[str, Any]) -> Dict[str, Any]: + if len(message["tool_calls"]) > 1: + raise ValueError("Expected only one tool call, but found multiple.") + return message["tool_calls"][0] + + +def display_tool_output( + tool_call_input: Dict[str, Any], tool_call_output: Dict[str, Any] +) -> None: + tool_expander = st.expander(label="Tool Calls:", expanded=False) + with tool_expander: + msg = ( + f"\n\nEnding tool: `{tool_call_input['name']}` with\n **args:**\n" + f"```\n{json.dumps(tool_call_input['args'], indent=2)}\n```\n" + f"\n\n**output:**\n " + f"```\n{json.dumps(tool_call_output, indent=2)}\n```" + ) + st.markdown(msg, unsafe_allow_html=True) + + +def handle_user_input(side_bar: SideBar) -> None: + prompt = st.chat_input() or st.session_state.modified_prompt + if prompt: + st.session_state.modified_prompt = None + parts = get_parts_from_files( + upload_gcs_checkbox=st.session_state.checkbox_state, + uploaded_files=side_bar.uploaded_files, + gcs_uris=side_bar.gcs_uris, + ) + st.session_state["gcs_uris_to_be_sent"] = "" + parts.append({"type": "text", "text": prompt}) + st.session_state.user_chats[st.session_state["session_id"]]["messages"].append( + HumanMessage(content=parts).model_dump() + ) + + display_user_input(parts) + generate_ai_response( + url_input_field=side_bar.url_input_field, + should_authenticate_request=side_bar.should_authenticate_request, + ) + update_chat_title() + if len(parts) > 1: + st.session_state.uploader_key += 1 + st.rerun() + + +def display_user_input(parts: List[Dict[str, Any]]) -> None: + human_message = st.chat_message("human") + with human_message: + existing_user_input = format_content(parts) + st.markdown(existing_user_input, unsafe_allow_html=True) + + +def generate_ai_response( + url_input_field: str, should_authenticate_request: bool +) -> None: + ai_message = st.chat_message("ai") + with ai_message: + status = st.status("Generating answerπŸ€–") + stream_handler = StreamHandler(st=st) + client = Client( + url=url_input_field, authenticate_request=should_authenticate_request + ) + get_chain_response(st=st, client=client, stream_handler=stream_handler) + status.update(label="Finished!", state="complete", expanded=False) + + +def update_chat_title() -> None: + if ( + st.session_state.user_chats[st.session_state["session_id"]]["title"] + == EMPTY_CHAT_NAME + ): + st.session_state.session_db.set_title( + st.session_state.user_chats[st.session_state["session_id"]] + ) + st.session_state.session_db.upsert_session( + st.session_state.user_chats[st.session_state["session_id"]] + ) + + +def display_feedback(side_bar: SideBar) -> None: + if st.session_state.run_id is not None: + feedback = streamlit_feedback( + feedback_type="faces", + optional_text_label="[Optional] Please provide an explanation", + key=f"feedback-{st.session_state.run_id}", + ) + if feedback is not None: + client = Client( + url=side_bar.url_input_field, + authenticate_request=side_bar.should_authenticate_request, + ) + client.log_feedback( + feedback_dict=feedback, + run_id=st.session_state.run_id, + ) + + +def main() -> None: + setup_page() + initialize_session_state() + side_bar = SideBar(st=st) + side_bar.init_side_bar() + display_messages() + handle_user_input(side_bar=side_bar) + display_feedback(side_bar=side_bar) + + +if __name__ == "__main__": + main() diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/style/app_markdown.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/style/app_markdown.py new file mode 100644 index 0000000000..b133258045 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/style/app_markdown.py @@ -0,0 +1,37 @@ +# 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. + +markdown_str = """ + +""" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/chat_utils.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/chat_utils.py new file mode 100644 index 0000000000..0ec34b0a6c --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/chat_utils.py @@ -0,0 +1,64 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +from typing import Any, Dict, List, Union + +import yaml + +SAVED_CHAT_PATH = str(os.getcwd()) + "/.saved_chats" + + +def preprocess_text(text: str) -> str: + if not text: + return text + + if text.startswith("\n"): + text = text[1:] + if text.endswith("\n"): + text = text[:-1] + return text + + +def fix_messages( + messages: List[Dict[str, Union[str, List[Dict[str, str]]]]] +) -> List[Dict[str, Union[str, List[Dict[str, str]]]]]: + for message in messages: + if isinstance(message["content"], list): + for part in message["content"]: + if part["type"] == "text": + part["text"] = preprocess_text(part["text"]) + else: + message["content"] = preprocess_text(message["content"]) + return messages + + +def save_chat(st: Any) -> None: + Path(SAVED_CHAT_PATH).mkdir(parents=True, exist_ok=True) + session_id = st.session_state["session_id"] + session = st.session_state.user_chats[session_id] + messages = session.get("messages", []) + if len(messages) > 0: + session["messages"] = fix_messages(session["messages"]) + filename = f"{session_id}.yaml" + with open(Path(SAVED_CHAT_PATH) / filename, "w") as file: + yaml.dump( + [session], + file, + allow_unicode=True, + default_flow_style=False, + encoding="utf-8", + ) + st.toast(f"Chat saved to path: ↓ {Path(SAVED_CHAT_PATH) / filename}") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/local_chat_history.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/local_chat_history.py new file mode 100644 index 0000000000..93fa3497c1 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/local_chat_history.py @@ -0,0 +1,116 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from datetime import datetime +from typing import Dict + +import yaml +from langchain_core.chat_history import BaseChatMessageHistory +from utils.title_summary import chain_title + + +class LocalChatMessageHistory(BaseChatMessageHistory): + def __init__( + self, + user_id: str, + session_id: str = "default", + base_dir: str = ".streamlit_chats", + ) -> None: + self.user_id = user_id + self.session_id = session_id + self.base_dir = base_dir + self.user_dir = os.path.join(self.base_dir, self.user_id) + self.session_file = os.path.join(self.user_dir, f"{session_id}.yaml") + + os.makedirs(self.user_dir, exist_ok=True) + + def get_session(self, session_id: str) -> None: + self.session_id = session_id + self.session_file = os.path.join(self.user_dir, f"{session_id}.yaml") + + def get_all_conversations(self) -> Dict[str, Dict]: + conversations = {} + for filename in os.listdir(self.user_dir): + if filename.endswith(".yaml"): + file_path = os.path.join(self.user_dir, filename) + with open(file_path, "r") as f: + conversation = yaml.safe_load(f) + if not isinstance(conversation, list) or len(conversation) > 1: + raise ValueError( + f"""Invalid format in {file_path}. + YAML file can only contain one conversation with the following + structure. + - messages: + - content: [message text] + - type: (human or ai)""" + ) + + conversation = conversation[0] + if "title" not in conversation: + conversation["title"] = filename + conversations[filename[:-5]] = conversation + return dict( + sorted(conversations.items(), key=lambda x: x[1].get("update_time", "")) + ) + + def upsert_session(self, session: Dict) -> None: + session["update_time"] = datetime.now().isoformat() + with open(self.session_file, "w") as f: + yaml.dump( + [session], + f, + allow_unicode=True, + default_flow_style=False, + encoding="utf-8", + ) + + def set_title(self, session: Dict) -> None: + """ + Set the title for the given session. + + This method generates a title for the session based on its messages. + If the session has messages, it appends a special message to prompt + for title creation, generates the title using a title chain, and + updates the session with the new title. + + Args: + session (dict): A dictionary containing session information, + including messages. + + Returns: + None + """ + if session["messages"]: + + messages = session["messages"] + [ + { + "type": "human", + "content": "End of conversation - Create one single title", + } + ] + # Remove the tool calls from conversation + messages = [ + msg + for msg in messages + if msg["type"] in ("ai", "human") and msg["content"] + ] + + title = chain_title.invoke(messages).content.strip() + session["title"] = title + self.upsert_session(session) + + def clear(self) -> None: + if os.path.exists(self.session_file): + os.remove(self.session_file) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/message_editing.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/message_editing.py new file mode 100644 index 0000000000..b6da34247c --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/message_editing.py @@ -0,0 +1,52 @@ +# 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 + + +class MessageEditing: + @staticmethod + def edit_message(st: Any, button_idx: int, message_type: str) -> None: + button_id = f"edit_box_{button_idx}" + if message_type == "human": + messages = st.session_state.user_chats[st.session_state["session_id"]][ + "messages" + ] + st.session_state.user_chats[st.session_state["session_id"]]["messages"] = ( + messages[:button_idx] + ) + st.session_state.modified_prompt = st.session_state[button_id] + else: + st.session_state.user_chats[st.session_state["session_id"]]["messages"][ + button_idx + ]["content"] = st.session_state[button_id] + + @staticmethod + def refresh_message(st: Any, button_idx: int, content: str) -> None: + messages = st.session_state.user_chats[st.session_state["session_id"]][ + "messages" + ] + st.session_state.user_chats[st.session_state["session_id"]]["messages"] = ( + messages[:button_idx] + ) + st.session_state.modified_prompt = content + + @staticmethod + def delete_message(st: Any, button_idx: int) -> None: + messages = st.session_state.user_chats[st.session_state["session_id"]][ + "messages" + ] + st.session_state.user_chats[st.session_state["session_id"]]["messages"] = ( + messages[:button_idx] + ) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/multimodal_utils.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/multimodal_utils.py new file mode 100644 index 0000000000..8109ce8958 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/multimodal_utils.py @@ -0,0 +1,216 @@ +# 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 base64 +from typing import Any, Dict, List, Optional, Union +from urllib.parse import quote + +from google.cloud import storage + +HELP_MESSAGE_MULTIMODALITY = ( + "For Gemini models to access the URIs you provide, store them in " + "Google Cloud Storage buckets within the same project used by Gemini." +) + +HELP_GCS_CHECKBOX = ( + "Enabling GCS upload will increase the app observability by avoiding" + " forwarding and logging large byte strings within the app." +) + + +def format_content(content: Union[str, List[Dict[str, Any]]]) -> str: + if isinstance(content, str): + return content + if len(content) == 1 and content[0]["type"] == "text": + return content[0]["text"] + else: + markdown = """Media: +""" + text = "" + for part in content: + if part["type"] == "text": + text = part["text"] + # Local Images: + if part["type"] == "image_url": + image_url = part["image_url"]["url"] + image_markdown = f'' + markdown = ( + markdown + + f""" +- {image_markdown} +""" + ) + if part["type"] == "media": + # Local other media + if "data" in part: + markdown = markdown + f"- Local media: {part['file_name']}\n" + # From GCS: + if "file_uri" in part: + # GCS images + if "image" in part["mime_type"]: + image_url = gs_uri_to_https_url(part["file_uri"]) + image_markdown = f'' + markdown = ( + markdown + + f""" +- {image_markdown} +""" + ) + # GCS other media + else: + image_url = gs_uri_to_https_url(part["file_uri"]) + markdown = ( + markdown + f"- Remote media: " + f"[{part['file_uri']}]({image_url})\n" + ) + markdown = ( + markdown + + f""" + +{text}""" + ) + return markdown + + +def get_gcs_blob_mime_type(gcs_uri: str) -> Optional[str]: + """Fetches the MIME type (content type) of a Google Cloud Storage blob. + + Args: + gcs_uri (str): The GCS URI of the blob in the format "gs://bucket-name/object-name". + + Returns: + str: The MIME type of the blob (e.g., "image/jpeg", "text/plain") if found, + or None if the blob does not exist or an error occurs. + """ + storage_client = storage.Client() + + try: + bucket_name, object_name = gcs_uri.replace("gs://", "").split("/", 1) + + bucket = storage_client.bucket(bucket_name) + blob = bucket.blob(object_name) + blob.reload() + return blob.content_type + + except Exception as e: + print(f"Error retrieving MIME type for {gcs_uri}: {e}") + return None # Indicate failure + + +def get_parts_from_files( + upload_gcs_checkbox: bool, uploaded_files: List[Any], gcs_uris: str +) -> List[Dict[str, Any]]: + parts = [] + # read from local directly + if not upload_gcs_checkbox: + for uploaded_file in uploaded_files: + im_bytes = uploaded_file.read() + if "image" in uploaded_file.type: + content = { + "type": "image_url", + "image_url": { + "url": f"data:{uploaded_file.type};base64," + f"{base64.b64encode(im_bytes).decode('utf-8')}" + }, + "file_name": uploaded_file.name, + } + else: + content = { + "type": "media", + "data": base64.b64encode(im_bytes).decode("utf-8"), + "file_name": uploaded_file.name, + "mime_type": uploaded_file.type, + } + + parts.append(content) + if gcs_uris != "": + for uri in gcs_uris.split(","): + content = { + "type": "media", + "file_uri": uri, + "mime_type": get_gcs_blob_mime_type(uri), + } + parts.append(content) + return parts + + +def upload_bytes_to_gcs( + bucket_name: str, + blob_name: str, + file_bytes: bytes, + content_type: Optional[str] = None, +) -> str: + """Uploads a bytes object to Google Cloud Storage and returns the GCS URI. + + Args: + bucket_name: The name of the GCS bucket. + blob_name: The desired name for the uploaded file in GCS. + file_bytes: The file's content as a bytes object. + content_type (optional): The MIME type of the file (e.g., "image/png"). + If not provided, GCS will try to infer it. + + Returns: + str: The GCS URI (gs://bucket_name/blob_name) of the uploaded file. + + Raises: + GoogleCloudError: If there's an issue with the GCS operation. + """ + storage_client = storage.Client() + bucket = storage_client.bucket(bucket_name) + blob = bucket.blob(blob_name) + blob.upload_from_string(data=file_bytes, content_type=content_type) + # Construct and return the GCS URI + gcs_uri = f"gs://{bucket_name}/{blob_name}" + return gcs_uri + + +def gs_uri_to_https_url(gs_uri: str) -> str: + """Converts a GS URI to an HTTPS URL without authentication. + + Args: + gs_uri: The GS URI in the format gs:///. + + Returns: + The corresponding HTTPS URL, or None if the GS URI is invalid. + """ + + if not gs_uri.startswith("gs://"): + raise ValueError("Invalid GS URI format") + + gs_uri = gs_uri[5:] + + # Extract bucket and object names, then URL encode the object name + bucket_name, object_name = gs_uri.split("/", 1) + object_name = quote(object_name) + + # Construct the HTTPS URL + https_url = f"https://storage.mtls.cloud.google.com/{bucket_name}/{object_name}" + return https_url + + +def upload_files_to_gcs(st: Any, bucket_name: str, files_to_upload: List[Any]) -> None: + bucket_name = bucket_name.replace("gs://", "") + uploaded_uris = [] + for file in files_to_upload: + if file: + file_bytes = file.read() + gcs_uri = upload_bytes_to_gcs( + bucket_name=bucket_name, + blob_name=file.name, + file_bytes=file_bytes, + content_type=file.type, + ) + uploaded_uris.append(gcs_uri) + st.session_state.uploader_key += 1 + st.session_state["gcs_uris_to_be_sent"] = ",".join(uploaded_uris) diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/stream_handler.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/stream_handler.py new file mode 100644 index 0000000000..c28c161c38 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/stream_handler.py @@ -0,0 +1,266 @@ +# 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 +from typing import Any, Dict, Generator, List, Optional +from urllib.parse import urljoin + +import google.auth +import google.auth.transport.requests +import google.oauth2.id_token +import requests +from google.auth.exceptions import DefaultCredentialsError +from langchain_core.messages import AIMessage, ToolMessage +from utils.multimodal_utils import format_content + +import streamlit as st + + +@st.cache_resource() +class Client: + """A client for streaming events from a server.""" + + def __init__(self, url: str, authenticate_request: bool = False) -> None: + """Initialize the Client with a base URL.""" + self.url = urljoin(url, "stream_events") + self.authenticate_request = authenticate_request + self.creds, _ = google.auth.default() + + if self.authenticate_request: + self.id_token = self.get_id_token(self.url) + + def get_id_token(self, url: str) -> str: + """ + Retrieves an ID token, attempting to use a service-to-service method first and + otherwise using user default credentials. + See more on Cloud Run authentication at this link: + https://cloud.google.com/run/docs/authenticating/service-to-service + Args: + url: The URL to use for the token request. + """ + + auth_req = google.auth.transport.requests.Request() + try: + token = google.oauth2.id_token.fetch_id_token(auth_req, url) + except DefaultCredentialsError: + self.creds.refresh(auth_req) + token = self.creds.id_token + return token + + def log_feedback(self, feedback_dict: Dict[str, Any], run_id: str) -> None: + score = feedback_dict["score"] + if score == "😞": + score = 0.0 + elif score == "πŸ™": + score = 0.25 + elif score == "😐": + score = 0.5 + elif score == "πŸ™‚": + score = 0.75 + elif score == "πŸ˜€": + score = 1.0 + feedback_dict["score"] = score + feedback_dict["run_id"] = run_id + feedback_dict["log_type"] = "feedback" + feedback_dict.pop("type") + url = urljoin(self.url, "feedback") + headers = { + "Content-Type": "application/json", + } + if self.authenticate_request: + headers["Authorization"] = f"Bearer {self.id_token}" + requests.post(url, data=json.dumps(feedback_dict), headers=headers) + + def stream_events( + self, data: Dict[str, Any] + ) -> Generator[Dict[str, Any], None, None]: + """Stream events from the server, yielding parsed event data.""" + headers = {"Content-Type": "application/json", "Accept": "text/event-stream"} + if self.authenticate_request: + headers["Authorization"] = f"Bearer {self.id_token}" + with requests.post( + self.url, json={"input": data}, headers=headers, stream=True + ) as response: + for line in response.iter_lines(): + if line: + try: + event = json.loads(line.decode("utf-8")) + yield event + except json.JSONDecodeError: + print(f"Failed to parse event: {line.decode('utf-8')}") + + +class StreamHandler: + """Handles streaming updates to a Streamlit interface.""" + + def __init__(self, st: Any, initial_text: str = "") -> None: + """Initialize the StreamHandler with Streamlit context and initial text.""" + self.st = st + self.tool_expander = st.expander("Tool Calls:", expanded=False) + self.container = st.empty() + self.text = initial_text + self.tools_logs = initial_text + + def new_token(self, token: str) -> None: + """Add a new token to the main text display.""" + self.text += token + self.container.markdown(format_content(self.text), unsafe_allow_html=True) + + def new_status(self, status_update: str) -> None: + """Add a new status update to the tool calls expander.""" + self.tools_logs += status_update + self.tool_expander.markdown(status_update) + + +class EventProcessor: + """Processes events from the stream and updates the UI accordingly.""" + + def __init__(self, st: Any, client: Client, stream_handler: StreamHandler) -> None: + """Initialize the EventProcessor with Streamlit context, client, and stream handler.""" + self.st = st + self.client = client + self.stream_handler = stream_handler + self.final_content = "" + self.tool_calls: List[Dict[str, Any]] = [] + self.additional_kwargs: Dict[str, Any] = {} + self.current_run_id: Optional[str] = None + + def process_events(self) -> None: + """Process events from the stream, handling each event type appropriately.""" + messages = self.st.session_state.user_chats[ + self.st.session_state["session_id"] + ]["messages"] + stream = self.client.stream_events( + data={ + "messages": messages, + "user_id": self.st.session_state["user_id"], + "session_id": self.st.session_state["session_id"], + } + ) + + event_handlers = { + "metadata": self.handle_metadata, + "end": self.handle_end, + "on_tool_start": self.handle_tool_start, + "on_retriever_start": self.handle_tool_start, + "on_tool_end": self.handle_tool_and_retriever_end, + "on_retriever_end": self.handle_tool_and_retriever_end, + "on_chat_model_stream": self.handle_chat_model_stream, + } + + for event in stream: + event_type = str(event.get("event")) + handler = event_handlers.get(event_type) + if handler: + handler(event) + + def handle_metadata(self, event: Dict[str, Any]) -> None: + """Handle metadata events.""" + self.current_run_id = event["data"].get("run_id") + + def handle_tool_start(self, event: Dict[str, Any]) -> None: + """Handle the start of a tool or retriever execution.""" + msg = ( + f"\n\nCalling tool: `{event['name']}` with args: `{event['data']['input']}`" + ) + self.stream_handler.new_status(msg) + + def handle_tool_and_retriever_end(self, event: Dict[str, Any]) -> None: + """Handle the end of a tool execution.""" + data = event["data"] + + # support for on_tool_end event + if isinstance(data["output"], dict): + tool_id = data["output"].get("tool_call_id") + tool_name = data["output"].get("name") + tool_output = data["output"] + + # support for on_retriever_end event + elif isinstance(data["output"], list): + tool_id = event.get("id", "retriever") + tool_name = event.get("name", event["event"]) + tool_output = {"tool_call_id": tool_name, "content": data["output"]} + else: + raise ValueError( + f"Unexpected data type for tool output: {type(data['output'])}" + ) + tool_input = data["input"] + tool_call_input = AIMessage( + content="", + tool_calls=[{"id": tool_id, "name": tool_name, "args": tool_input}], + ) + tool_call_output = ToolMessage(**tool_output) + self.tool_calls.append(tool_call_input.model_dump()) + self.tool_calls.append(tool_call_output.model_dump()) + msg = ( + f"\n\nEnding tool: `{tool_name}` with\n **args:**\n" + f"```\n{json.dumps(tool_input, indent=2)}\n```\n" + f"\n\n**output:**\n " + f"```\n{json.dumps(tool_output, indent=2)}\n```" + ) + self.stream_handler.new_status(msg) + + def handle_chat_model_stream(self, event: Dict[str, Any]) -> None: + """Handle incoming tokens from the chat model stream.""" + data = event["data"] + content = data["chunk"]["content"] + self.additional_kwargs = { + **self.additional_kwargs, + **data["chunk"]["additional_kwargs"], + } + if content and len(content.strip()) > 0: + self.final_content += content + self.stream_handler.new_token(content) + + def handle_end(self, event: Dict[str, Any]) -> None: + """Handle the end of the event stream and finalize the response.""" + final_message = AIMessage( + content=self.final_content, + id=self.current_run_id, + additional_kwargs=self.additional_kwargs, + ).model_dump() + session = self.st.session_state["session_id"] + self.st.session_state.user_chats[session]["messages"] = ( + self.st.session_state.user_chats[session]["messages"] + self.tool_calls + ) + self.st.session_state.user_chats[session]["messages"].append(final_message) + self.st.session_state.run_id = self.current_run_id + + +def get_chain_response(st: Any, client: Client, stream_handler: StreamHandler) -> None: + """Process the chain response update the Streamlit UI. + + This function initiates the event processing for a chain of operations, + involving an AI model's response generation and potential tool calls. + It creates an EventProcessor instance and starts the event processing loop. + + Args: + st (Any): The Streamlit app instance, used for accessing session state + and updating the UI. + client (Client): An instance of the Client class used to stream events + from the server. + stream_handler (StreamHandler): An instance of the StreamHandler class + used to update the Streamlit UI with + streaming content. + + Returns: + None + + Side effects: + - Updates the Streamlit UI with streaming tokens and tool call information. + - Modifies the session state to include the final AI message and run ID. + - Handles various events like chain starts/ends, tool calls, and model outputs. + """ + processor = EventProcessor(st, client, stream_handler) + processor.process_events() diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/title_summary.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/title_summary.py new file mode 100644 index 0000000000..443263c1a4 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/streamlit/utils/title_summary.py @@ -0,0 +1,66 @@ +# 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 + +llm = ChatVertexAI(model_name="gemini-1.5-flash-001", temperature=0) + +# fmt: off +# ruff: noqa: E501 +title_template = ChatPromptTemplate.from_messages( + [("system", """Given a list of messages between a human and AI, come up with a short and relevant title for the conversation. Use up to 10 words. The title needs to be concise. +Examples: +**Input:** +``` +Human: hi, what is the best italian dish? +AI: That's a tough one! Italy has so many amazing dishes, it's hard to pick just one "best." To help me give you a great suggestion, tell me a little more about what you're looking for. +``` +**Output:** Best italian dish + +**Input:** + +``` +Human: How to fix a broken laptop screen? +AI: Fixing a broken laptop screen can be tricky and often requires professional help. However, there are a few things you can try at home before resorting to a repair shop. +``` + +**Output:** Fixing a broken laptop screen + +**Input:** + +``` +Human: Can you write me a poem about the beach? +AI: As the sun dips down below the horizon +And the waves gently kiss the shore, +I sit here and watch the ocean +And feel its power evermore. +``` + +**Output:** Poem about the beach + +**Input:** + +``` +Human: What's the best way to learn to code? +AI: There are many ways to learn to code, and the best method for you will depend on your learning style and goals. +``` + +**Output:** How to learn to code +"""), + + MessagesPlaceholder(variable_name="messages"), + ]) + +chain_title = title_template | llm diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_langgraph_dummy_agent.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_langgraph_dummy_agent.py new file mode 100644 index 0000000000..cf30ea2d1e --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_langgraph_dummy_agent.py @@ -0,0 +1,62 @@ +# 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 pytest +from langchain_core.messages import AIMessageChunk, HumanMessage + +from app.patterns.langgraph_dummy_agent.chain import chain +from app.utils.output_types import OnToolEndEvent + +CHAIN_NAME = "LangGraph agent" + + +@pytest.mark.asyncio +async def test_langgraph_chain_astream_events() -> None: + """ + Integration testing example for the default LangGraph agent chain. We assert that the chain returns + events, containing AIMessageChunks. + """ + user_message = HumanMessage(f"Test message for {CHAIN_NAME} chain") + input_dict = {"messages": [user_message]} + + events = [event async for event in chain.astream_events(input_dict, version="v2")] + + assert len(events) > 1, ( + f"Expected multiple events for {CHAIN_NAME} chain, " f"got {len(events)}" + ) + on_tool_end_events = [event for event in events if event["event"] == "on_tool_end"] + assert len(on_tool_end_events) == 1, ( + f"Expected exactly one on_tool_end event for {CHAIN_NAME} chain, " + f"got {len(on_tool_end_events)}" + ) + assert isinstance( + OnToolEndEvent.model_validate(on_tool_end_events[0]), OnToolEndEvent + ) + + on_chain_stream_events = [ + event for event in events if event["event"] == "on_chat_model_stream" + ] + + assert on_chain_stream_events, ( + f"Expected at least one on_chat_model_stream event" f" for {CHAIN_NAME} chain" + ) + + for event in on_chain_stream_events: + assert AIMessageChunk.model_validate( + event["data"]["chunk"] + ), f"Invalid AIMessageChunk for {CHAIN_NAME} chain: {event['data']['chunk']}" + + logging.info(f"All assertions passed for {CHAIN_NAME} chain") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_rag_qa.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_rag_qa.py new file mode 100644 index 0000000000..1c84e0bbb5 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/patterns/test_rag_qa.py @@ -0,0 +1,63 @@ +# 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 pytest +from langchain_core.messages import AIMessageChunk, HumanMessage + +from app.patterns.custom_rag_qa.chain import chain +from app.utils.output_types import OnToolEndEvent + +CHAIN_NAME = "Rag QA" + + +@pytest.mark.asyncio +async def test_rag_chain_astream_events() -> None: + """ + Integration testing example for the default RAG QA chain. We assert that the chain returns events, + containing AIMessageChunks. + """ + user_message = HumanMessage(f"Test message for {CHAIN_NAME} chain") + input_dict = {"messages": [user_message]} + + events = [event async for event in chain.astream_events(input_dict, version="v2")] + + assert len(events) > 1, ( + f"Expected multiple events for {CHAIN_NAME} chain, " f"got {len(events)}" + ) + + on_tool_end_events = [event for event in events if event["event"] == "on_tool_end"] + assert len(on_tool_end_events) == 1, ( + f"Expected exactly one on_tool_end event for {CHAIN_NAME} chain, " + f"got {len(on_tool_end_events)}" + ) + assert isinstance( + OnToolEndEvent.model_validate(on_tool_end_events[0]), OnToolEndEvent + ) + + on_chain_stream_events = [ + event for event in events if event["event"] == "on_chat_model_stream" + ] + + assert on_chain_stream_events, ( + f"Expected at least one on_chat_model_stream event" f" for {CHAIN_NAME} chain" + ) + + for event in on_chain_stream_events: + assert AIMessageChunk.model_validate( + event["data"]["chunk"] + ), f"Invalid AIMessageChunk for {CHAIN_NAME} chain: {event['data']['chunk']}" + + logging.info(f"All assertions passed for {CHAIN_NAME} chain") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_chain.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_chain.py new file mode 100644 index 0000000000..9ff36b52a7 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_chain.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 pytest +from langchain_core.messages import AIMessageChunk, HumanMessage + +from app.chain import chain + +CHAIN_NAME = "Default" + + +@pytest.mark.asyncio +async def test_default_chain_astream_events() -> None: + """ + Integration testing example for the default dummy chain. We assert that the chain returns events, + containing AIMessageChunks. + """ + user_message = HumanMessage(f"Test message for {CHAIN_NAME} chain") + input_dict = {"messages": [user_message]} + + events = [event async for event in chain.astream_events(input_dict, version="v2")] + + assert len(events) > 1, ( + f"Expected multiple events for {CHAIN_NAME} chain, " f"got {len(events)}" + ) + + on_chain_stream_events = [ + event for event in events if event["event"] == "on_chat_model_stream" + ] + + assert on_chain_stream_events, ( + f"Expected at least one on_chat_model_stream event" f" for {CHAIN_NAME} chain" + ) + + for event in on_chain_stream_events: + assert AIMessageChunk.model_validate( + event["data"]["chunk"] + ), f"Invalid AIMessageChunk for {CHAIN_NAME} chain: {event['data']['chunk']}" + + logging.info(f"All assertions passed for {CHAIN_NAME} chain") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_server_e2e.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_server_e2e.py new file mode 100644 index 0000000000..c71fec246e --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/integration/test_server_e2e.py @@ -0,0 +1,174 @@ +# 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 subprocess +import sys +import threading +import time +import uuid +from typing import Any, Iterator + +import pytest +import requests +from requests.exceptions import RequestException + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +BASE_URL = "http://127.0.0.1:8000/" +STREAM_EVENTS_URL = BASE_URL + "stream_events" +FEEDBACK_URL = BASE_URL + "feedback" + +HEADERS = {"Content-Type": "application/json"} + + +def log_output(pipe: Any, log_func: Any) -> None: + """Log the output from the given pipe.""" + for line in iter(pipe.readline, ""): + log_func(line.strip()) + + +def start_server() -> subprocess.Popen[str]: + """Start the FastAPI server using subprocess and log its output.""" + command = [ + sys.executable, + "-m", + "uvicorn", + "app.server:app", + "--host", + "0.0.0.0", + "--port", + "8000", + ] + process = subprocess.Popen( + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1 + ) + + # Start threads to log stdout and stderr in real-time + threading.Thread( + target=log_output, args=(process.stdout, logger.info), daemon=True + ).start() + threading.Thread( + target=log_output, args=(process.stderr, logger.error), daemon=True + ).start() + + return process + + +def wait_for_server(timeout: int = 60, interval: int = 1) -> bool: + """Wait for the server to be ready.""" + start_time = time.time() + while time.time() - start_time < timeout: + try: + response = requests.get("http://127.0.0.1:8000/docs") + if response.status_code == 200: + logger.info("Server is ready") + return True + except RequestException: + pass + time.sleep(interval) + logger.error(f"Server did not become ready within {timeout} seconds") + return False + + +@pytest.fixture(scope="session") +def server_fixture(request: Any) -> Iterator[subprocess.Popen[str]]: + """Pytest fixture to start and stop the server for testing.""" + logger.info("Starting server process") + server_process = start_server() + if not wait_for_server(): + pytest.fail("Server failed to start") + logger.info("Server process started") + + def stop_server() -> None: + logger.info("Stopping server process") + server_process.terminate() + server_process.wait() + logger.info("Server process stopped") + + request.addfinalizer(stop_server) + yield server_process + + +def test_chat_stream(server_fixture: subprocess.Popen[str]) -> None: + """Test the chat stream functionality.""" + logger.info("Starting chat stream test") + + data = { + "input": { + "messages": [ + {"type": "human", "content": "Hello, AI!"}, + {"type": "ai", "content": "Hello!"}, + {"type": "human", "content": "What cooking recipes do you suggest?"}, + ], + "user_id": "test-user", + "session_id": "test-session", + } + } + + response = requests.post( + STREAM_EVENTS_URL, headers=HEADERS, json=data, stream=True, timeout=10 + ) + assert response.status_code == 200 + + events = [json.loads(line) for line in response.iter_lines() if line] + logger.info(f"Received {len(events)} events") + + assert len(events) > 2, f"Expected more than 2 events, got {len(events)}." + assert events[0]["event"] == "metadata", ( + f"First event should be 'metadata', " f"got {events[0]['event']}" + ) + assert "run_id" in events[0]["data"], "Missing 'run_id' in metadata" + + event_types = [event["event"] for event in events] + assert "on_chat_model_stream" in event_types, "Missing 'on_chat_model_stream' event" + assert events[-1]["event"] == "end", ( + f"Last event should be 'end', " f"got {events[-1]['event']}" + ) + + logger.info("Test completed successfully") + + +def test_chat_stream_error_handling(server_fixture: subprocess.Popen[str]) -> None: + """Test the chat stream error handling.""" + logger.info("Starting chat stream error handling test") + + data = {"input": [{"type": "invalid_type", "content": "Cause an error"}]} + response = requests.post( + STREAM_EVENTS_URL, headers=HEADERS, json=data, stream=True, timeout=10 + ) + + assert response.status_code == 422, ( + f"Expected status code 422, " f"got {response.status_code}" + ) + logger.info("Error handling test completed successfully") + + +def test_collect_feedback(server_fixture: subprocess.Popen[str]) -> None: + """ + Test the feedback collection endpoint (/feedback) to ensure it properly + logs the received feedback. + """ + # Create sample feedback data + feedback_data = { + "score": 4, + "run_id": str(uuid.uuid4()), + "text": "Great response!", + } + + response = requests.post(FEEDBACK_URL, json=feedback_data, headers=HEADERS) + assert response.status_code == 200 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/.results/.placeholder b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/.results/.placeholder new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/README.md b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/README.md new file mode 100644 index 0000000000..23fda0224a --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/README.md @@ -0,0 +1,79 @@ +# Robust Load Testing for Generative AI Applications + +This directory provides a comprehensive load testing framework for your Generative AI application, leveraging the power of [Locust](http://locust.io), a leading open-source load testing tool. + +## Local Load Testing + +Follow these steps to execute load tests on your local machine: + +**1. Start the FastAPI Server:** + +Launch the FastAPI server in a separate terminal: + +```bash +poetry run uvicorn app.server:app --host 0.0.0.0 --port 8000 --reload +``` + +**2. (In another tab) Create virtual environment with Locust** +Using another terminal tab, This is suggested to avoid conflicts with the existing application python environment. + +```commandline +python3 -m venv locust_env && source locust_env/bin/activate && pip install locust==2.31.1 +``` + +**3. Execute the Load Test:** +Trigger the Locust load test with the following command: + +```bash +locust -f tests/load_test/load_test.py \ +-H http://127.0.0.1:8000 \ +--headless \ +-t 30s -u 60 -r 2 \ +--csv=tests/load_test/.results/results \ +--html=tests/load_test/.results/report.html +``` + +This command initiates a 30-second load test, simulating 2 users spawning per second, reaching a maximum of 60 concurrent users. + +**Results:** + +Comprehensive CSV and HTML reports detailing the load test performance will be generated and saved in the `tests/load_test/.results` directory. + +## Remote Load Testing (Targeting Cloud Run) + +This framework also supports load testing against remote targets, such as a staging Cloud Run instance. This process is seamlessly integrated into the Continuous Delivery pipeline via Cloud Build, as defined in the [pipeline file](cicd/cd/staging.yaml). + +**Prerequisites:** + +- **Dependencies:** Ensure your environment has the same dependencies required for local testing. +- **Cloud Run Invoker Role:** You'll need the `roles/run.invoker` role to invoke the Cloud Run service. + +**Steps:** + +**1. Obtain Cloud Run Service URL:** + +Navigate to the Cloud Run console, select your service, and copy the URL displayed at the top. Set this URL as an environment variable: + +```bash +export RUN_SERVICE_URL=https://your-cloud-run-service-url.run.app +``` + +**2. Obtain ID Token:** + +Retrieve the ID token required for authentication: + +```bash +export _ID_TOKEN=$(gcloud auth print-identity-token -q) +``` + +**3. Execute the Load Test:** +The following command executes the same load test parameters as the local test but targets your remote Cloud Run instance. + +```bash +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 +``` diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/load_test.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/load_test.py new file mode 100644 index 0000000000..2880ebe697 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/load_test/load_test.py @@ -0,0 +1,81 @@ +# 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 os +import time + +from locust import HttpUser, between, task + + +class ChatStreamUser(HttpUser): + wait_time = between(1, 3) # Wait 1-3 seconds between tasks + + @task + def chat_stream(self) -> None: + headers = {"Content-Type": "application/json"} + if os.environ.get("_ID_TOKEN"): + headers["Authorization"] = f'Bearer {os.environ["_ID_TOKEN"]}' + + data = { + "input": { + "messages": [ + {"type": "human", "content": "Hello, AI!"}, + {"type": "ai", "content": "Hello!"}, + {"type": "human", "content": "Who are you?"}, + ], + "user_id": "test-user", + "session_id": "test-session", + } + } + + start_time = time.time() + + with self.client.post( + "/stream_events", + headers=headers, + json=data, + catch_response=True, + name="/stream_events first event", + stream=True, + ) as response: + if response.status_code == 200: + events = [] + for line in response.iter_lines(): + if line: + events.append(json.loads(line)) + if events[-1]["event"] == "end": + break + + end_time = time.time() + total_time = end_time - start_time + + if ( + len(events) > 2 + and events[0]["event"] == "metadata" + and events[-1]["event"] == "end" + ): + response.success() + self.environment.events.request.fire( + request_type="POST", + name="/stream_events end", + response_time=total_time * 1000, # Convert to milliseconds + response_length=len(json.dumps(events)), + response=response, + context={}, + ) + else: + response.failure("Unexpected response structure") + else: + response.failure(f"Unexpected status code: {response.status_code}") diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_server.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_server.py new file mode 100644 index 0000000000..c545749a27 --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_server.py @@ -0,0 +1,133 @@ +import json +import logging +import os +from typing import Any, Generator +from unittest.mock import MagicMock, patch + +import pytest +from google.auth.credentials import Credentials +from httpx import AsyncClient +from langchain_core.messages import HumanMessage + +from app.utils.input_types import InputChat + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +@pytest.fixture(autouse=True) +def mock_google_cloud_credentials() -> Generator[None, None, None]: + with patch.dict( + os.environ, + { + "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/mock/credentials.json", + "GOOGLE_CLOUD_PROJECT_ID": "mock-project-id", + }, + ): + yield + + +@pytest.fixture(autouse=True) +def mock_google_auth_default() -> Generator[None, None, None]: + mock_credentials = MagicMock(spec=Credentials) + mock_project = "mock-project-id" + + with patch("google.auth.default", return_value=(mock_credentials, mock_project)): + yield + + +@pytest.fixture +def sample_input_chat() -> InputChat: + """ + Fixture to create a sample input chat for testing. + """ + return InputChat( + user_id="test-user", + session_id="test-session", + messages=[HumanMessage(content="What is the meaning of life?")], + ) + + +class AsyncIterator: + """ + A helper class to create asynchronous iterators for testing. + """ + + def __init__(self, seq: list) -> None: + self.iter = iter(seq) + + def __aiter__(self) -> "AsyncIterator": + return self + + async def __anext__(self) -> Any: + try: + return next(self.iter) + except StopIteration: + raise StopAsyncIteration + + +def test_redirect_root_to_docs() -> None: + """ + Test that the root endpoint (/) redirects to the Swagger UI documentation. + """ + from fastapi.testclient import TestClient + + from app.server import app + + client = TestClient(app) + response = client.get("/") + assert response.status_code == 200 + assert "Swagger UI" in response.text + + +@pytest.mark.asyncio +async def test_stream_chat_events() -> None: + """ + Test the stream_events endpoint to ensure it correctly handles + streaming responses and generates the expected events. + """ + from app.server import app + + input_data = { + "input": { + "user_id": "test-user", + "session_id": "test-session", + "messages": [ + {"type": "human", "content": "Hello, AI!"}, + {"type": "ai", "content": "Hello!"}, + {"type": "human", "content": "What cooking recipes do you suggest?"}, + ], + } + } + + mock_uuid = "12345678-1234-5678-1234-567812345678" + mock_events = [ + {"event": "on_chat_model_stream", "data": {"content": "Mocked response"}}, + {"event": "on_chat_model_stream", "data": {"content": "Additional response"}}, + ] + + with patch("app.server.chain") as mock_chain: + mock_chain.astream_events.return_value = AsyncIterator(mock_events) + + with patch("uuid.uuid4", return_value=mock_uuid), patch( + "app.server.Traceloop.set_association_properties" + ): + async with AsyncClient(app=app, base_url="http://test") as ac: + response = await ac.post("/stream_events", json=input_data) + + assert response.status_code == 200 + assert response.headers["content-type"] == "text/event-stream; charset=utf-8" + + events = [] + for event in response.iter_lines(): + events.append(json.loads(event)) + + assert len(events) == 4 + assert events[0]["event"] == "metadata" + assert events[0]["data"]["run_id"] == str(mock_uuid) + assert events[1]["event"] == "on_chat_model_stream" + assert events[1]["data"]["content"] == "Mocked response" + assert events[2]["event"] == "on_chat_model_stream" + assert events[2]["data"]["content"] == "Additional response" + assert events[3]["event"] == "end" diff --git a/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_utils/test_tracing_exporter.py b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_utils/test_tracing_exporter.py new file mode 100644 index 0000000000..071f5b162f --- /dev/null +++ b/gemini/sample-apps/e2e-gen-ai-app-starter-kit/tests/unit/test_utils/test_tracing_exporter.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. + +from typing import Any, Generator +from unittest.mock import Mock, patch + +import pytest +from google.cloud import logging as google_cloud_logging +from google.cloud import storage +from opentelemetry.sdk.trace import ReadableSpan + +from app.utils.tracing import CloudTraceLoggingSpanExporter + + +@pytest.fixture +def mock_logging_client() -> Mock: + return Mock(spec=google_cloud_logging.Client) + + +@pytest.fixture +def mock_storage_client() -> Mock: + return Mock(spec=storage.Client) + + +@pytest.fixture +def mock_credentials() -> Any: + return Mock() + + +@pytest.fixture +def patch_auth(mock_credentials: Any) -> Generator[Mock, None, None]: + with patch( + "google.auth.default", return_value=(mock_credentials, "project") + ) as mock_auth: + yield mock_auth + + +@pytest.fixture +def patch_clients( + mock_logging_client: Mock, mock_storage_client: Mock +) -> Generator[None, None, None]: + with patch("google.cloud.logging.Client", return_value=mock_logging_client): + with patch("google.cloud.storage.Client", return_value=mock_storage_client): + yield + + +@pytest.fixture +def exporter( + mock_logging_client: Mock, + mock_storage_client: Mock, + patch_auth: Any, + mock_credentials: Any, + patch_clients: Any, +) -> CloudTraceLoggingSpanExporter: + exporter = CloudTraceLoggingSpanExporter( + project_id="test-project", + logging_client=mock_logging_client, + storage_client=mock_storage_client, + bucket_name="test-bucket", + ) + exporter._ensure_bucket_exists = Mock() # type: ignore[method-assign] + return exporter + + +def test_init(exporter: CloudTraceLoggingSpanExporter) -> None: + assert exporter.project_id == "test-project" + assert exporter.bucket_name == "test-bucket" + assert exporter.debug is False + + +def test_store_in_gcs(exporter: CloudTraceLoggingSpanExporter) -> None: + span_id = "test-span-id" + content = "test-content" + uri = exporter.store_in_gcs(content, span_id) + assert uri == f"gs://test-bucket/spans/{span_id}.json" + exporter.bucket.blob.assert_called_once_with(f"spans/{span_id}.json") + + +@patch("json.dumps") +def test_process_large_attributes_small_payload( + mock_json_dumps: Mock, exporter: CloudTraceLoggingSpanExporter +) -> None: + mock_json_dumps.return_value = "a" * 100 # Small payload + span_dict = {"attributes": {"key": "value"}} + result = exporter._process_large_attributes(span_dict, "span-id") + assert result == span_dict + + +@patch("json.dumps") +def test_process_large_attributes_large_payload( + mock_json_dumps: Mock, exporter: CloudTraceLoggingSpanExporter +) -> None: + mock_json_dumps.return_value = "a" * (400 * 1024 + 1) # Large payload + span_dict = { + "attributes": { + "key1": "value1", + "traceloop.association.properties.key2": "value2", + } + } + result = exporter._process_large_attributes(span_dict, "span-id") + assert "uri_payload" in result["attributes"] + assert "url_payload" in result["attributes"] + assert "key1" not in result["attributes"] + assert "traceloop.association.properties.key2" in result["attributes"] + + +@patch.object(CloudTraceLoggingSpanExporter, "_process_large_attributes") +def test_export( + mock_process_large_attributes: Mock, exporter: CloudTraceLoggingSpanExporter +) -> None: + mock_span = Mock(spec=ReadableSpan) + mock_span.get_span_context.return_value.trace_id = 123 + mock_span.get_span_context.return_value.span_id = 456 + mock_span.to_json.return_value = '{"key": "value"}' + + mock_process_large_attributes.return_value = {"processed": "data"} + + exporter.export([mock_span]) + + mock_process_large_attributes.assert_called_once() + exporter.logger.log_struct.assert_called_once()