docs: Feast integration

Signed-off-by: Anush008 <anushshetty90@gmail.com>
2026-09-29 07:58:31 +02:00 · 2024-11-07 01:53:39 +05:30
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 ## Framework Integrations

-| Framework                             | Description                                                                                          |
-| ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| Framework                                                     | Description                                                                                          |
+| ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
 | [AutoGen](/documentation/frameworks/autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.             |
 | [Canopy](/documentation/frameworks/canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                |
 | [Cheshire Cat](/documentation/frameworks/cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                    |
 | [DocArray](/documentation/frameworks/docarray/)               | Python library for managing data in multi-modal AI applications.                                     |
 | [DSPy](/documentation/frameworks/dspy/)                       | Framework for algorithmically optimizing LM prompts and weights.                                     |
+| [Feast](/documentation/frameworks/feast/)                     | Open-source feature store to operate production ML systems at scale as a set of features.            |
 | [Fifty-One](/documentation/frameworks/fifty-one/)             | Toolkit for building high-quality datasets and computer vision models.                               |
 | [Genkit](/documentation/frameworks/genkit/)                   | Framework to build, deploy, and monitor production-ready AI-powered apps.                            |
 | [Haystack](/documentation/frameworks/haystack/)               | LLM orchestration framework to build customizable, production-ready LLM applications.                |
@@ -25,6 +26,6 @@ weight: 20
 | [Pandas-AI](/documentation/frameworks/pandas-ai/)             | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language        |
 | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.             |
 | [Spring AI](/documentation/frameworks/spring-ai/)             | Java AI framework for building with Spring design principles such as portability and modular design. |
-| [Sycamore](/documentation/frameworks/sycamore/)             | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
+| [Sycamore](/documentation/frameworks/sycamore/)               | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
 | [txtai](/documentation/frameworks/txtai/)                     | Python library for semantic search, LLM orchestration and language model workflows.                  |
 | [Vanna AI](/documentation/frameworks/vanna-ai/)               | Python RAG framework for SQL generation and querying.                                                |
@@ -0,0 +1,63 @@
+---
+title: Feast
+---
+
+## Feast
+
+[Feast (**Fe**ature **St**ore)](https://docs.feast.dev) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.
+
+Qdrant is available as a supported vectorstore in Feast to integrate in your workflows.
+
+## Insatallation
+
+To use the Qdrant online store, you need to install Feast with the `qdrant` extra.
+
+```bash
+pip install 'feast[qdrant]'
+```
+
+## Usage
+
+An example config with Qdrant could look like:
+
+```yaml
+project: my_feature_repo
+registry: data/registry.db
+provider: local
+online_store:
+    type: qdrant
+    host: xyz-example.eu-central.aws.cloud.qdrant.io
+    port: 6333
+    api_key: <your-own-key>
+    vector_len: 384
+    # Reference: https://qdrant.tech/documentation/concepts/vectors/#named-vectors
+    # vector_name: text-vec
+    write_batch_size: 100
+```
+
+You can refer to the Feast [reference](https://rtd.feast.dev/en/master/index.html#) for the full list of configuration options.
+
+## Retrieving Documents
+
+The Qdrant online store supports retrieving document vectors for a given list of entity keys. The document vectors are returned as a dictionary where the key is the entity key and the value being the vector.
+
+```python
+from feast import FeatureStore
+
+feature_store = FeatureStore(repo_path="feature_store.yaml")
+
+query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
+top_k = 5
+
+feature_values = feature_store.retrieve_online_documents(
+    feature="my_feature",
+    query=query_vector,
+    top_k=top_k
+)
+```
+
+## 📚 Further Reading
+
+- [Feast Docs](http://docs.feast.dev/)
+- [Feast Reference](https://rtd.feast.dev/en/master/index.html/)
+- [Source](https://github.com/feast-dev/feast/tree/master/sdk/python/feast/infra/online_stores/)