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