Add GenAI documentation page to Introduction section

Co-Authored-By: Francisco Javier Arceo <farceo@redhat.com>
feast-dev · franciscojavierarceo · Jun 2, 2025 · May 24, 2025 · May 24, 2025 · May 24, 2025
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+# Feast for Generative AI
+
+## Overview
+
+Feast provides robust support for Generative AI applications, enabling teams to build, deploy, and manage feature infrastructure for Large Language Models (LLMs) and other generative AI systems. With Feast's vector database integrations and feature management capabilities, teams can implement production-ready Retrieval Augmented Generation (RAG) systems and other GenAI applications with the same reliability and operational excellence as traditional ML systems.
+
+## Key Capabilities for GenAI
+
+### Vector Database Support
+
+Feast integrates with popular vector databases to store and retrieve embedding vectors efficiently:
+
+* **Milvus**: Full support for vector similarity search with the `retrieve_online_documents_v2` method
+* **SQLite**: Local vector storage and retrieval for development and testing
+* **Elasticsearch**: Scalable vector search capabilities
+* **Postgres with PGVector**: SQL-based vector operations
+* **Qdrant**: Purpose-built vector database integration
+
+These integrations allow you to:
+- Store document embeddings as features
+- Perform vector similarity search to find relevant context
+- Retrieve both vector embeddings and traditional features in a single API call
+
+### Retrieval Augmented Generation (RAG)
+
+Feast simplifies building RAG applications by providing:
+
+1. **Document embedding storage**: Store and version document embeddings alongside your other features
+2. **Vector similarity search**: Find the most relevant documents for a given query
+3. **Feature retrieval**: Combine document embeddings with structured features for richer context
+4. **Versioning and governance**: Track changes to your document repository over time
+
+The typical RAG workflow with Feast involves:
+
+```
+┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
+│  Document   │     │  Document   │     │    Feast    │     │     LLM     │
+│  Processing │────▶│  Embedding  │────▶│   Feature   │────▶│   Context   │
+│             │     │             │     │    Store    │     │  Generation  │
+└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
+```
+
+### Feature Transformation for LLMs
+
+Feast supports on-demand transformations that can be used to:
+
+* Process raw text into embeddings
+* Chunk documents for more effective retrieval
+* Normalize and preprocess features before serving to LLMs
+
+## Getting Started with Feast for GenAI
+
+### Installation
+
+To use Feast with vector database support, install with the appropriate extras:
+
+```bash
+# For Milvus support
+pip install feast[milvus,nlp]
+
+# For Elasticsearch support
+pip install feast[elasticsearch]
+
+# For Qdrant support
+pip install feast[qdrant]
+
+# For SQLite support (Python 3.10 only)
+pip install feast[sqlite_vec]
+```
+
+### Configuration
+
+Configure your feature store to use a vector database as the online store:
+
+```yaml
+project: genai-project
+provider: local
+registry: data/registry.db
+online_store:
+  type: milvus
+  path: data/online_store.db
+  vector_enabled: true
+  embedding_dim: 384  # Adjust based on your embedding model
+  index_type: "IVF_FLAT"
+
+offline_store:
+  type: file
+entity_key_serialization_version: 3
+```
+
+### Defining Vector Features
+
+Create feature views with vector index support:
+
+```python
+from feast import FeatureView, Field, Entity
+from feast.types import Array, Float32, String
+
+document = Entity(
+    name="document_id",
+    description="Document identifier",
+    join_keys=["document_id"],
+)
+
+document_embeddings = FeatureView(
+    name="document_embeddings",
+    entities=[document],
+    schema=[
+        Field(
+            name="vector",
+            dtype=Array(Float32),
+            vector_index=True,  # Enable vector search
+            vector_search_metric="COSINE",  # Similarity metric
+        ),
+        Field(name="document_id", dtype=String),
+        Field(name="content", dtype=String),
+    ],
+    source=document_source,
+    ttl=timedelta(days=30),
+)
+```
+
+### Retrieving Similar Documents
+
+Use the `retrieve_online_documents_v2` method to find similar documents:
+
+```python
+# Generate query embedding
+query = "How does Feast support vector databases?"
+query_embedding = embed_text(query)  # Your embedding function
+
+# Retrieve similar documents
+context_data = store.retrieve_online_documents_v2(
+    features=[
+        "document_embeddings:vector",
+        "document_embeddings:document_id",
+        "document_embeddings:content",
+    ],
+    query=query_embedding,
+    top_k=3,
+    distance_metric='COSINE',
+).to_df()
+```
+
+## Use Cases
+
+### Document Question-Answering
+
+Build document Q&A systems by:
+1. Storing document chunks and their embeddings in Feast
+2. Converting user questions to embeddings
+3. Retrieving relevant document chunks
+4. Providing these chunks as context to an LLM
+
+### Knowledge Base Augmentation
+
+Enhance your LLM's knowledge by:
+1. Storing company-specific information as embeddings
+2. Retrieving relevant information based on user queries
+3. Injecting this information into the LLM's context
+
+### Semantic Search
+
+Implement semantic search by:
+1. Storing document embeddings in Feast
+2. Converting search queries to embeddings
+3. Finding semantically similar documents using vector search
+
+## Learn More
+
+For more detailed information and examples:
+
+* [Vector Database Reference](reference/alpha-vector-database.md)
+* [RAG Tutorial with Docling](tutorials/rag-with-docling.md)
+* [Milvus Quickstart Example](https://github.com/feast-dev/feast/tree/master/examples/rag/milvus-quickstart.ipynb)