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feat: Add feature view curl generator #5415

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franciscojavierarceo merged 6 commits into feast-dev:master from franciscojavierarceo:devin/1748887778-feature-view-curl-generator

Jun 3, 2025

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feat: Add feature view curl generator #5415

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eafc197

Add GenAI documentation page to Introduction section

devin-ai-integration[bot] May 24, 2025

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Move GenAI page to getting-started directory and update SUMMARY.md

devin-ai-integration[bot] May 24, 2025

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franciscojavierarceo May 24, 2025

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Add unstructured data transformation and Spark integration details to…

devin-ai-integration[bot] May 24, 2025

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franciscojavierarceo May 27, 2025

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feat: Add CURL Generator tab to Feature View pages

devin-ai-integration[bot] Jun 2, 2025

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Add GenAI documentation page to Introduction section

Co-Authored-By: Francisco Javier Arceo <[email protected]>

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devin-ai-integration[bot] and franciscojavierarceo committed Jun 2, 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)

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