https://github.com/arjunprabhulal/vertex-ai-rag
Google Cloud Vertex AI RAG
https://github.com/arjunprabhulal/vertex-ai-rag
Last synced: about 2 months ago
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Google Cloud Vertex AI RAG
- Host: GitHub
- URL: https://github.com/arjunprabhulal/vertex-ai-rag
- Owner: arjunprabhulal
- Created: 2025-03-21T03:45:39.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-21T03:50:08.000Z (about 2 months ago)
- Last Synced: 2025-03-21T04:31:17.212Z (about 2 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vertex AI RAG with Gemini 2 Flash
A complete implementation of Retrieval-Augmented Generation (RAG) workflow using Google Cloud's Vertex AI and Gemini 2.0 Flash.
## Step-by-Step RAG Workflow
1. Create and manage RAG corpus in Vertex AI
2. Import documents from Google Cloud Storage
3. Configure chunking and embedding models
4. Perform direct context retrieval
5. Generate AI responses enhanced with document context using Gemini models
6. Clean up RAG corpora after demo
## Requirements
- Google Cloud account with Vertex AI access
- Python 3.8+
- Required Python packages (see requirements.txt)
## Setup
1. Enable required Google Cloud services:
```bash
gcloud services enable aiplatform.googleapis.com --project=PROJECT_ID
gcloud services enable storage.googleapis.com --project=PROJECT_ID
```
2. Set up IAM permissions:
```bash
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:[email protected]" --role="roles/aiplatform.user"
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:[email protected]" --role="roles/storage.objectAdmin"
```
3. Create a Google Cloud Storage bucket and upload your PDF files:
```bash
# Create a new GCS bucket (skip if you already have one)
gsutil mb -l us-central1 gs://your-bucket-name
# Upload PDF files to the bucket
gsutil cp your-document.pdf gs://your-bucket-name/
# Verify files were uploaded successfully
gsutil ls gs://your-bucket-name/
```
Note: Remember the path to your files (e.g., `gs://your-bucket-name/your-document.pdf`) as you'll need it for the corpus creation.
4. Set up authentication credentials:
```bash
# Download your service account key from GCP Console and set the environment variable
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/service-account-key
# On Windows, use:
# set GOOGLE_APPLICATION_CREDENTIALS=C:\path\to\your\service-account-key
```
Note: This step is critical for authenticating with Google Cloud services. You need a service account with the appropriate permissions.
5. Clone this repository:
```bash
git clone https://github.com/arjunprabhulal/vertex-ai-rag.git
cd vertex-ai-rag
```
6. Install dependencies:
```
pip install -r requirements.txt
pip install --upgrade google-cloud-aiplatform
```
7. Update the configuration in `vertex_ai_rag.py`:
- Set your PROJECT_ID
- Configure your corpus display name
- Add paths to your documents in Google Cloud Storage:
```python
paths = ["gs://your-bucket-name/your-document.pdf"]
```
## Usage
Run the main script:
```
python vertex_ai_rag.py
```
## Documentation
For more information on Vertex AI RAG, see the official [Google Cloud documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-overview).
## Author
For more articles on AI/ML and Generative AI, follow me on Medium: https://medium.com/@arjun-prabhulal