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Copy file name to clipboardExpand all lines: docs/getting-started/genai.md
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- Efficiently materializing features to vector databases
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- Scaling RAG applications to enterprise-level document repositories
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### Scaling with Ray Integration
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Feast integrates with Ray to enable distributed processing for RAG applications:
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***Ray Compute Engine**: Distributed feature computation using Ray's task and actor model
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***Ray Offline Store**: Process large document collections and generate embeddings at scale
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***Ray Batch Materialization**: Efficiently materialize features from offline to online stores
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***Distributed Embedding Generation**: Scale embedding generation across multiple nodes
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This integration enables:
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- Distributed processing of large document collections
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- Parallel embedding generation for millions of text chunks
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- Kubernetes-native scaling for RAG applications
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- Efficient resource utilization across multiple nodes
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- Production-ready distributed RAG pipelines
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For detailed information on building distributed RAG applications with Feast and Ray, see [Feast + Ray: Distributed Processing for RAG Applications](https://feast.dev/blog/feast-ray-distributed-processing/).
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## Model Context Protocol (MCP) Support
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Feast supports the Model Context Protocol (MCP), which enables AI agents and applications to interact with your feature store through standardized MCP interfaces. This allows seamless integration with LLMs and AI agents for GenAI applications.
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* [RAG Tutorial with Docling](../tutorials/rag-with-docling.md)
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* [RAG Fine Tuning with Feast and Milvus](../../examples/rag-retriever/README.md)
Copy file name to clipboardExpand all lines: docs/reference/compute-engine/ray.md
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The Ray compute engine is a distributed compute implementation that leverages [Ray](https://www.ray.io/) for executing feature pipelines including transformations, aggregations, joins, and materializations. It provides scalable and efficient distributed processing for both `materialize()` and `get_historical_features()` operations.
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## Quick Start with Ray Template
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### Ray RAG Template - Batch Embedding at Scale
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For RAG (Retrieval-Augmented Generation) applications with distributed embedding generation:
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```bash
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feast init -t ray_rag my_rag_project
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cd my_rag_project/feature_repo
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```
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The Ray RAG template demonstrates:
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-**Parallel Embedding Generation**: Uses Ray compute engine to generate embeddings across multiple workers
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-**Vector Search Integration**: Works with Milvus for semantic similarity search
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-**Complete RAG Pipeline**: Data → Embeddings → Search workflow
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The Ray compute engine automatically distributes the embedding generation across available workers, making it ideal for processing large datasets efficiently.
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## Overview
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The Ray compute engine provides:
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### With Feature Transformations
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#### On-Demand Transformations
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```python
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from feast import FeatureView, Field
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from feast.types import Float64
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```
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#### Ray Native Transformations
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For distributed transformations that leverage Ray's dataset and parallel processing capabilities, use `mode="ray"` in your `BatchFeatureView`:
For more information, see the [Ray documentation](https://docs.ray.io/en/latest/) and [Ray Data guide](https://docs.ray.io/en/latest/data/getting-started.html).
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## Functionality Matrix
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There are currently four core offline store implementations: `DaskOfflineStore`, `BigQueryOfflineStore`, `SnowflakeOfflineStore`, and `RedshiftOfflineStore`.
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There are several additional implementations contributed by the Feast community (`PostgreSQLOfflineStore`, `SparkOfflineStore`, and `TrinoOfflineStore`), which are not guaranteed to be stable or to match the functionality of the core implementations.
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There are several additional implementations contributed by the Feast community (`PostgreSQLOfflineStore`, `SparkOfflineStore`, `TrinoOfflineStore`, and `RayOfflineStore`), which are not guaranteed to be stable or to match the functionality of the core implementations.
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Details for each specific offline store, such as how to configure it in a `feature_store.yaml`, can be found [here](README.md).
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Below is a matrix indicating which offline stores support which methods.
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The Ray offline store is a data I/O implementation that leverages [Ray](https://www.ray.io/) for reading and writing data from various sources. It focuses on efficient data access operations, while complex feature computation is handled by the [Ray Compute Engine](../compute-engine/ray.md).
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## Quick Start with Ray Template
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The easiest way to get started with Ray offline store is to use the built-in Ray template:
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```bash
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feast init -t ray my_ray_project
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cd my_ray_project/feature_repo
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```
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This template includes:
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- Pre-configured Ray offline store and compute engine setup
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- Sample feature definitions optimized for Ray processing
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- Demo workflow showcasing Ray capabilities
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- Resource settings for local development
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The template provides a complete working example with sample datasets and demonstrates both Ray offline store data I/O operations and Ray compute engine distributed processing.
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