wip

gemini/sample-apps/conversational-genai-app-template/CONTRIBUTING.md

@@ -4,11 +4,13 @@ We'd love to accept your patches and contributions to this sample. There are
 just a few small guidelines you need to follow.
 
 ## Contributor License Agreement
-Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.
+
+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
@@ -26,8 +28,8 @@ 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.
@@ -37,10 +39,8 @@ 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. 
-
-

gemini/sample-apps/conversational-genai-app-template/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 
-RUN pip install poetry==1.6.1
+RUN pip install  --no-cache-dir poetry==1.6.1
 
 RUN poetry config virtualenvs.create false
 
@@ -14,4 +14,4 @@ RUN poetry install  --no-interaction --no-ansi --no-dev
 
 EXPOSE 8080
 
-CMD exec uvicorn app.server:app --host 0.0.0.0 --port 8080
+CMD ["uvicorn", "app.server:app", "--host", "0.0.0.0", "--port", "8080"]

gemini/sample-apps/conversational-genai-app-template/app/README.md

@@ -4,7 +4,7 @@ This folder implements a chatbot application using FastAPI, and Google Cloud ser
 
 ## Folder Structure
 
-```
+```plaintext
 .
 ├── server.py           # Main FastAPI server
 ├── chain.py            # Default chain implementation
@@ -14,6 +14,7 @@ This folder implements a chatbot application using FastAPI, and Google Cloud ser
 ├── utils/              # Utility functions and classes
 └── eval/               # Evaluation tools and data
 ```
+
 ## Generative AI Application Patterns
 
 ### 1. Default Chain
@@ -38,7 +39,6 @@ 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)
@@ -47,29 +47,21 @@ All chains have the same interface, allowing for seamless swapping without chang
 
 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).
+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.** 
+- 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).
+Events are forwarded to BigQuery through a [log router](https://cloud.google.com/logging/docs/routing/overview) for long-term storage and analysis. The deployment of the log router is done via Terraform code in [deployment/terraform](../deployment/terraform).
 
 ### Looker Studio Dashboard
 
-Once the data is written to BigQuery, it can be used to populate a [Looker Studio dashboard](TODO: Add link). 
+Once the data is written to BigQuery, it can be used to populate a [Looker Studio dashboard](TODO: Add link).
 
 This dashboard, offered as a template, provides a starting point for building custom visualizations on the top of the data being captured.
-
-
-
-
-
-
-
-

gemini/sample-apps/conversational-genai-app-template/deployment/README.md

@@ -1,9 +1,11 @@
-## Deployment README.md
+# 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
+
 ![Deployment Workflow](../images/deployment_workflow.png)
 
 **Description:**
@@ -24,8 +26,7 @@ The application leverages [**Terraform**](http://terraform.io) to define and pro
    - Deploys to production environment
 
 
-
-### Setup
+## Setup
 
 **Prerequisites:**
 
@@ -40,7 +41,7 @@ The application leverages [**Terraform**](http://terraform.io) to define and pro
    gcloud services enable serviceusage.googleapis.com cloudresourcemanager.googleapis.com cloudbuild.googleapis.com secretmanager.googleapis.com
    ```
 
-### Step-by-Step Guide
+## Step-by-Step Guide
 
 1. **Create a Git Repository using your favorite git provider (GitHub, GitLab, Bitbucket, etc.)**
 
@@ -82,7 +83,8 @@ Other optional variables include: telemetry and feedback BigQuery dataset IDs, l
 After completing these steps, your infrastructure will be set up and ready for deployment!
 
 
-### Dev 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:
@@ -98,7 +100,7 @@ gcloud run deploy conversational-app-sample --source . --project $YOUR_DEV_PROJE
 ```
 
 
-**E2E Demo video**
+### E2E Demo video
 
 <a href="https://storage.googleapis.com/test-elia-us-central1/template%20deployment%20demo.mp4">
   <img src="../images/preview_video.png" alt="Watch the video" width="300"/>