Scalable Infrastructure for RAG-based Generative AI with Vertex AI
Vertex AI offers a powerful suite of tools to build Retrieval-Augmented Generation (RAG) capable generative AI applications.
Vertex AI offers a powerful suite of tools, including Vertex AI Search and Generative AI models like PaLM, to streamline the development of RAG-capable generative AI applications.
- By leveraging Vertex AI’s capabilities, you can build RAG applications that deliver more accurate, informative, and contextually-aware outputs compared to traditional generative models.
- Vertex AI can be particularly beneficial for building RAG applications suited for drone video analysis, as described in the research paper:
- Enhanced Accuracy with Contextual Search:
- Vertex AI Search: This component within Vertex AI leverages Google Search technology for semantic retrieval. It can be integrated with the system to find relevant video segments based on a user’s query. This retrieved video data can then be used to provide context to the Large Language Model (LLM) within the RAG architecture. With this contextual grounding, the LLM can generate more accurate and informative responses to the user’s query.
Improved Information Retrieval for Diverse Data:
- Flexibility for Different Data Types: Vertex AI Search can handle various data types, including text, code, and structured data. This makes it suitable for the proposed system, which likely deals not only with the video data itself but also with associated geospatial and temporal metadata. By including this metadata in the search process, Vertex AI can retrieve even more relevant video segments for the LLM to analyze.
Scalability for Large Datasets:
- Vertex AI’s Managed Infrastructure: Vertex AI provides a managed infrastructure for building and deploying machine learning models. This can be crucial for handling the potentially large volumes of drone video data the system might encounter. With Vertex AI, you can focus on developing the core functionalities of the system without worrying about managing underlying infrastructure.
In essence, Vertex AI acts as a powerful backbone for the RAG application in this context. By leveraging its search capabilities and managed infrastructure, the system can achieve:
- More informative responses: The LLM receives a richer context through relevant video segments retrieved by Vertex AI Search, leading to more informative responses to user queries.
- Improved accuracy: Contextual grounding helps the LLM generate more accurate outputs by aligning its responses with the specific content of the drone videos.
- Efficient handling of diverse data: Vertex AI Search can effectively handle the various data types involved in drone video analysis, including video content, geospatial data, and temporal information.
- Target Audience: “For developers looking to build next-generation generative AI applications, Vertex AI provides a user-friendly and scalable platform to implement Retrieval-Augmented Generation techniques.”
Architecture
The following diagram shows a high-level view of an architecture for a RAG-capable generative AI application in Google Cloud:
The salient features breakdown of the infrastructure you can set up using Vertex AI:
Data Ingestion Subsystem:
- Cloud Storage: Stores your training data, including prompts for quality evaluation.
- Pub/Sub: Triggers a Cloud Run job whenever new data is uploaded to Cloud Storage.
- Cloud Run: Runs a serverless function to process the uploaded data and prepare it for training.
Training and Model Management:
- Vertex AI Training: Trains your RAG model on the prepared data.
- Vertex AI Endpoints: Deploys your trained model for real-time inference.
- Vertex Model Registry: Stores, manages, and versions your trained models for easy tracking and deployment.
Inference and Serving:
- Cloud Run or Cloud Functions: Can be used to host an API endpoint that interacts with the deployed model.
- API Gateway: Provides a managed API layer for secure and scalable access to your model.
Additional Considerations:
- BigQuery: Can be used to store and analyze large datasets for model training evaluation.
- AlloyDB for PostgreSQL: Can be used to store metadata or manage user interactions with the application (optional).
Security and Compliance:
- Leverage Vertex AI’s built-in security features for data encryption, access control, and network security.
- Configure Cloud Storage and BigQuery for appropriate access permissions.
Benefits:
- Scalability: Vertex AI and Cloud Run offer a serverless architecture that scales automatically with demand.
- Cost-Effectiveness: Pay-per-use model helps optimize costs based on your application’s usage.
- Manageability: Vertex AI simplifies model training, deployment, and management.
Further Resources:
- Google Cloud Documentation: https://cloud.google.com/architecture/rag-capable-gen-ai-app-using-vertex-ai
Conclusion
Vertex AI empowers you to build Retrieval-Augmented Generation (RAG) applications that deliver outputs that are 20% more accurate and significantly more informative compared to standalone Generative Pre-trained Transformers (GPTs). This improvement stems from Vertex AI’s ability to provide relevant context through its Search capabilities, allowing RAG applications to generate responses that are more grounded in factual information.
