initial support for redis vector db as coded by augment

Author: Yuval Levy

docs/reference/alpha-vector-database.md

@@ -15,6 +15,7 @@ Below are supported vector databases and implemented features:
 | Faiss           | [ ]       | [ ]      | []          | []          |
 | SQLite          | [x]       | [ ]      | [x]         | [x]         |
 | Qdrant          | [x]       | [x]      | []          | []          |
+| Redis           | [x]       | [x]      | [x]         | [x]         |
 
 *Note: V2 Support means the SDK supports retrieval of features along with vector embeddings from vector similarity search.
 
@@ -189,7 +190,7 @@ print('\n'.join([c.message.content for c in response.choices]))
 
 ### Configuration and Installation
 
-We offer [Milvus](https://milvus.io/), [PGVector](https://github.com/pgvector/pgvector), [SQLite](https://github.com/asg017/sqlite-vec), [Elasticsearch](https://www.elastic.co) and [Qdrant](https://qdrant.tech/) as Online Store options for Vector Databases.
+We offer [Milvus](https://milvus.io/), [PGVector](https://github.com/pgvector/pgvector), [SQLite](https://github.com/asg017/sqlite-vec), [Elasticsearch](https://www.elastic.co), [Qdrant](https://qdrant.tech/), and [Redis](https://redis.io/) as Online Store options for Vector Databases.
 
 Milvus offers a convenient local implementation for vector similarity search. To use Milvus, you can install the Feast package with the Milvus extra.
 
@@ -209,6 +210,18 @@ pip install feast[elasticsearch]
 ```bash
 pip install feast[qdrant]
 ```
+
+#### Installation with Redis
+
+```bash
+pip install feast[redis]
+```
+
+Note: Redis vector search requires Redis with RediSearch module. You can use:
+- Redis Stack (includes RediSearch)
+- Redis Enterprise with RediSearch module
+- Redis with RediSearch module installed
+
 #### Installation with SQLite
 
 If you are using `pyenv` to manage your Python versions, you can install the SQLite extension with the following command:

docs/reference/online-stores/redis.md

@@ -79,6 +79,65 @@ online_store:
 
 The full set of configuration options is available in [RedisOnlineStoreConfig](https://rtd.feast.dev/en/latest/#feast.infra.online_stores.redis.RedisOnlineStoreConfig).
 
+## Vector Search Support
+
+Redis online store supports vector similarity search through RediSearch. To enable vector search capabilities:
+
+{% code title="feature_store.yaml" %}
+```yaml
+project: my_feature_repo
+registry: data/registry.db
+provider: local
+online_store:
+  type: redis
+  connection_string: "localhost:6379"
+  vector_enabled: true
+  vector_dim: 384
+  vector_index_type: "FLAT"  # or "HNSW"
+  vector_distance_metric: "COSINE"  # or "L2", "IP"
+```
+{% endcode %}
+
+### Vector Configuration Options
+
+- `vector_enabled`: Whether to enable vector search capabilities (default: False)
+- `vector_dim`: Vector dimension - must be specified if vector_enabled is True
+- `vector_index_type`: Vector index type - "FLAT" or "HNSW" (default: "FLAT")
+- `vector_distance_metric`: Distance metric - "COSINE", "L2", or "IP" (default: "COSINE")
+- `hnsw_m`: Max number of outgoing edges for HNSW index (default: 16)
+- `hnsw_ef_construction`: Max number of connected neighbors during HNSW graph building (default: 200)
+- `hnsw_ef_runtime`: Max top candidates during HNSW KNN search (default: 10)
+
+### Prerequisites
+
+Vector search requires Redis with RediSearch module. You can use:
+- Redis Stack (includes RediSearch)
+- Redis Enterprise with RediSearch module
+- Redis with RediSearch module installed
+
+### Retrieving online document vectors
+
+The Redis online store supports retrieving document vectors for vector similarity search:
+
+{% code title="python" %}
+```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
+
+# Retrieve the top k closest features to the query vector
+feature_values = feature_store.retrieve_online_documents_v2(
+    features=["my_feature_view:vector_field", "my_feature_view:metadata"],
+    query=query_vector,
+    top_k=top_k,
+    distance_metric="COSINE"
+)
+```
+{% endcode %}
+
 ## Functionality Matrix
 
 The set of functionality supported by online stores is described in detail [here](overview.md#functionality).

examples/redis_vector_search/example_repo.py

@@ -0,0 +1,32 @@
+from datetime import timedelta
+from feast import Entity, FeatureView, Field, FileSource
+from feast.types import Array, Float32, Int64, String, UnixTimestamp
+
+# Define entities
+item = Entity(name="item_id", join_keys=["item_id"])
+
+# Define data source
+documents_source = FileSource(
+    name="documents_source",
+    path="data/documents.parquet",
+    timestamp_field="event_timestamp",
+)
+
+# Define feature view with vector field
+document_embeddings = FeatureView(
+    name="document_embeddings",
+    entities=[item],
+    schema=[
+        Field(
+            name="vector",
+            dtype=Array(Float32),
+            vector_index=True,
+            vector_search_metric="COSINE",
+        ),
+        Field(name="item_id", dtype=Int64),
+        Field(name="content", dtype=String),
+        Field(name="event_timestamp", dtype=UnixTimestamp),
+    ],
+    source=documents_source,
+    ttl=timedelta(hours=24),
+)

examples/redis_vector_search/feature_store.yaml

@@ -0,0 +1,18 @@
+project: redis_vector_search
+provider: local
+registry: data/registry.db
+online_store:
+  type: redis
+  connection_string: "localhost:6379"
+  vector_enabled: true
+  vector_dim: 384
+  vector_index_type: "FLAT"
+  vector_distance_metric: "COSINE"
+
+offline_store:
+  type: file
+
+entity_key_serialization_version: 3
+
+auth:
+  type: no_auth