GitHub repo showcases RAG examples for Feast framework

Feast offers a robust framework for enhancing retrieval-augmented generation (RAG) applications by integrating document processing, vector database storage, and feature management into a cohesive system. This quickstart guide demonstrates how combining Feast with Milvus for vector storage and Docling for PDF processing creates a powerful foundation for building sophisticated LLM applications that leverage both structured and unstructured data.

The big picture: Feast provides a declarative infrastructure for RAG applications that streamlines how developers manage document processing and retrieval for large language models.

• The framework enables real-time access to precomputed document embeddings while maintaining version control and reusability across teams.
• By integrating with Milvus as a vector database, the system can efficiently perform similarity searches to find contextually relevant information.
• Docling handles the transformation of PDFs into text data that can be embedded and utilized by LLMs during the ingestion process.

Why this matters: RAG applications fundamentally improve LLM performance by providing relevant contextual information, but building the underlying data infrastructure has traditionally been complex.

• This approach gives data scientists a standardized way to ship scalable RAG applications with all the operational benefits of a feature store.
• Teams can collaborate using discoverable, versioned feature transformations rather than building siloed, redundant systems.

Key components: The project demonstrates a complete end-to-end workflow for building and deploying RAG applications.

• The data directory contains demo content including Wikipedia summaries of cities with sentence embeddings stored in Parquet format.
• The example repository defines feature views and entity configurations that structure how data is processed and served.
• Two notebooks demonstrate the practical implementation: one showing Docling’s PDF extraction capabilities and another showcasing how Feast handles the ingestion and retrieval process.

Technical implementation: The project uses a local development configuration that can be adapted for production environments.

• Feature definitions and entity configurations are managed in the example_repo.py file.
• The feature_store.yaml configures both offline storage (using local files) and online retrieval (using Milvus Lite).
• The architecture allows for injecting both embeddings and traditional features into LLM prompts, providing richer contextual information.