Optimize your RAG and agentic systems with metadata using Deasy Labs and Google Cloud
AI Summary
Summary Note for Video: Enhancing RAG and Agentic Workflows with DeasyLabs and Google Cloud
Objective: Enhance Retrieval-Augmented Generation (RAG) and agentic workflows with improved metadata management.
Key Features:
- Enrich Unstructured Data: Use descriptive metadata to improve AI context.
- Bespoke Metadata Hierarchies: Automatically derive tailored metadata without manual input.
- Contextualization: Curate and filter unstructured data to boost general AI applications.
Use Case Example:
- A scenario where an engineer builds a RAG system with an initial accuracy of 70%. DeasyLabs’ orchestration increases model accuracy by enriching data context for user queries.
Benefits of DeasyLabs:
- Auto-derive metadata taxonomies specific to AI applications.
- Extract, standardize, and refine metadata over time.
- Use metadata for pre-filtering and enhancing results.
- Maintain metadata integrity as new data is added.
Integration with Vertex AI: Build more effective applications utilizing RAG and Gemini for data classifications and handling larger data volumes.
Call to Action: Schedule a demo at deasylabs.com.