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+# Vertex AI Search accessed via Google Cloud Functions
+
+This example is based on the
+[Python client for the Vertex AI Search API](https://cloud.google.com/generative-ai-app-builder/docs/libraries#client-libraries-usage-python),
+which will get search results, snippets, metadata, and the LLM summary grounded
+on search results. This is implemented in the `vertex_ai_search_client.py` file.
+
+That functionality is exposed on a REST API which is implemented in `main.py`
+intended to be deployed to a Google Cloud Function using an HTTPS trigger on a
+Python 3 runtime;
+[read more here](https://cloud.google.com/functions/docs/samples/functions-http-content#functions_http_content-python).
+
+**[Read more about Vertex AI Search accessed via Google Cloud Functions](../)**
+
+## Environment Variables
+
+The following environment variables are required for both local development and
+deployment:
+
+- `PROJECT_ID`: Your Google Cloud project ID
+- `LOCATION`: The location of your Vertex AI Search data store
+- `DATA_STORE_ID`: The ID of your Vertex AI Search data store
+- `ENGINE_DATA_TYPE`: Type of data in the engine (0-3)
+- `ENGINE_CHUNK_TYPE`: Type of chunking used (0-3)
+- `SUMMARY_TYPE`: Type of summary used (0-3)
+
+## Local Development
+
+### Setup
+
+1. Ensure you have the Google Cloud SDK installed and configured.
+2. Clone this repository and navigate to the project directory.
+3. Set up your environment variables:
+
+```bash
+gcloud auth login
+bash setup_env.sh
+```
+
+Alternatively, you can manually create and edit a `.env` file with the required
+variables.
+
+### Run locally
+
+Run this code locally via **Functions Framework** or **Functions Emulator**;
+[read more about running cloud functions locally](https://cloud.google.com/functions/docs/running/overview).
+
+```bash
+pip install -r requirements.txt
+pip install functions-framework
+functions-framework --target=vertex_ai_search
+```
+
+In a different terminal, execute a `POST` search query based on your data:
+
+```bash
+export SEARCH_TERM="What is the ... for ...?"
+curl -m 310 -X POST localhost:8080 \
+-H "Content-Type: application/json" \
+-d "{\"search_term\": \"${SEARCH_TERM}\"}"
+```
+
+### Run tests
+
+#### Unit tests
+
+These tests mock the API interactions and should run quickly:
+
+```bash
+pip install pytest
+pytest test_vertex_ai_search_client.py
+```
+
+#### Integration tests
+
+These tests actually call the Vertex AI Search API and depend on your data
+stores being configured in Vertex AI Search:
+
+```bash
+pip install pytest
+pytest test_integration_vertex_ai_search_client.py
+```
+
+## Deployment
+
+To deploy this function to Google Cloud:
+
+1. Ensure you have set up the required environment variables (see Environment
+   Variables section).
+2. Run the following command:
+
+```bash
+gcloud functions deploy vertex_ai_search --runtime python39 --trigger-http --allow-unauthenticated
+```
+
+You will get back a URL for triggering the function.
+
+## Usage
+
+After deployment, you can use the function as follows:
+
+```bash
+curl -X POST https://YOUR_FUNCTION_URL \
+-H "Content-Type: application/json" \
+-d '{"search_term": "your search query"}'
+```
+
+Replace `YOUR_FUNCTION_URL` with the URL of your deployed function, and fill in
+the search query.
+
+If you run into problems, go to
+[Google Cloud Functions](https://console.cloud.google.com/functions), find the
+function you just deployed, and review the logs for informative errors. Perhaps
+you need to setup
+[Google Cloud IAM](https://cloud.google.com/functions/docs/reference/iam) roles
+or permissions.
+
+## Customization
+
+This implementation provides a basic way to access and control your queries to
+the Vertex AI Search API. It simplifies CORS and bearer token authentication,
+and allows for some minor customization of inputs and outputs.
+
+If you require more extensive customization, consider using an orchestration
+framework like [LangChain](https://www.langchain.com/) or
+[LlamaIndex](https://www.llamaindex.ai/) which have Vertex AI Search
+integrations.