GeezerVison

The World Wide Web, restored to 800 pixels of glory.

GeezerVison is showing github.com — docs: Add Feast-powered AI agent example with MCP, persistent memory,… · feast-dev/feast@705df00

File tree

Expand file treeCollapse file tree

Open diff view settings

Filter options

docs
- SUMMARY.md
- getting-started
  - genai.md
examples/agent_feature_store
- README.md
- agent.py
- feature_repo
  - feature_store.yaml
  - features.py
- run_demo.sh
- setup_data.py
infra/website
- docs/blog
  - feast-agents-mcp.md
- public/images/blog

Expand file treeCollapse file tree

Open diff view settings

Collapse file

`‎docs/SUMMARY.md‎`

Copy file name to clipboardExpand all lines: docs/SUMMARY.md

+1Lines changed: 1 addition & 0 deletions

Display the source diff
Display the rich diff

Original file line number	Diff line number	Diff line change
`@@ -55,6 +55,7 @@`
`55`	`55`	`* [Retrieval Augmented Generation (RAG) with Feast](tutorials/rag-with-docling.md)`
`56`	`56`	`* [RAG Fine Tuning with Feast and Milvus](../examples/rag-retriever/README.md)`
`57`	`57`	`* [MCP - AI Agent Example](../examples/mcp_feature_store/README.md)`
	`58`	`+* [Feast-Powered AI Agent](../examples/agent_feature_store/README.md)`
`58`	`59`
`59`	`60`	`## How-to Guides`
`60`	`61`

Collapse file

`‎docs/getting-started/genai.md‎`

Copy file name to clipboardExpand all lines: docs/getting-started/genai.md

+16-1Lines changed: 16 additions & 1 deletion

Display the source diff
Display the rich diff

Original file line number	Diff line number	Diff line change
`@@ -89,6 +89,17 @@ Implement semantic search by:`
`89`	`89`	`2. Converting search queries to embeddings`
`90`	`90`	`3. Finding semantically similar documents using vector search`
`91`	`91`
	`92`	`+### AI Agents with Context and Memory`
	`93`	`+`
	`94`	`+Feast can serve as both the context provider and persistent memory layer for AI agents. Unlike stateless RAG pipelines, agents make autonomous decisions about which tools to call and can write state back to the feature store:`
	`95`	`+`
	`96`	`+1. Structured context: Retrieve customer profiles, account data, and other entity-keyed features`
	`97`	`+2. Knowledge retrieval: Search vector embeddings for relevant documents`
	`98`	+3. Persistent memory: Store and recall per-entity interaction history (last topic, resolution, preferences) using `write_to_online_store`
	`99`	`+4. Governed access: All reads and writes are subject to the same RBAC, TTL, and audit policies as any other feature`
	`100`	`+`
	`101`	`+With MCP enabled, agents built with any framework (LangChain, LlamaIndex, CrewAI, AutoGen, or custom) can discover and call Feast tools dynamically. See the [Feast-Powered AI Agent example](../../examples/agent_feature_store/) and the blog post [Building AI Agents with Feast](https://feast.dev/blog/feast-agents-mcp/) for a complete walkthrough.`
	`102`	`+`
`92`	`103`	`### Scaling with Spark Integration`
`93`	`104`
`94`	`105`	`Feast integrates with Apache Spark to enable large-scale processing of unstructured data for GenAI applications:`
@@ -167,9 +178,11 @@ The MCP integration uses the `fastapi_mcp` library to automatically transform yo
`167`	`178`	`The fastapi_mcp integration automatically exposes your Feast feature server's FastAPI endpoints as MCP tools. This means AI assistants can:`
`168`	`179`
`169`	`180`	* Call `/get-online-features` to retrieve features from the feature store
	`181`	+* Call `/retrieve-online-documents` to perform vector similarity search
	`182`	+* Call `/write-to-online-store` to persist agent state (memory, notes, interaction history)
`170`	`183`	* Use `/health` to check server status
`171`	`184`
`172`		`-For a complete example, see the [MCP Feature Store Example](../../examples/mcp_feature_store/).`
	`185`	`+For a basic MCP example, see the [MCP Feature Store Example](../../examples/mcp_feature_store/). For a full agent with persistent memory, see the [Feast-Powered AI Agent Example](../../examples/agent_feature_store/).`
`173`	`186`
`174`	`187`	`## Learn More`
`175`	`188`
`@@ -181,6 +194,8 @@ For more detailed information and examples:`
`181`	`194`	`* [Milvus Quickstart Example](https://github.com/feast-dev/feast/tree/master/examples/rag/milvus-quickstart.ipynb)`
`182`	`195`	`* [Feast + Ray: Distributed Processing for RAG Applications](https://feast.dev/blog/feast-ray-distributed-processing/)`
`183`	`196`	`* [MCP Feature Store Example](../../examples/mcp_feature_store/)`
	`197`	`+* [Feast-Powered AI Agent Example (with Memory)](../../examples/agent_feature_store/)`
	`198`	`+* [Blog: Building AI Agents with Feast](https://feast.dev/blog/feast-agents-mcp/)`
`184`	`199`	`* [MCP Feature Server Reference](../reference/feature-servers/mcp-feature-server.md)`
`185`	`200`	`* [Spark Data Source](../reference/data-sources/spark.md)`
`186`	`201`	`* [Spark Offline Store](../reference/offline-stores/spark.md)`

0 commit comments

Comments

0 (0)

You are visitor 01402520 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.