feat: Added docling and pytorch as add on

Signed-off-by: ntkathole <[email protected]>

README.md

@@ -202,7 +202,7 @@ The list below contains the functionality that contributors are planning to deve
   * [x] Python Client
   * [x] [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server)
   * [x] [Java feature server (alpha)](https://github.com/feast-dev/feast/blob/master/infra/charts/feast/README.md)
-  * [x] [Go feature server (alpha)](https://github.com/feast-dev/feast/blob/master/go/README.md)
+  * [x] [Go feature server (alpha)](https://docs.feast.dev/reference/feature-servers/go-feature-server)
 * **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
   * [x] Data profiling and validation (Great Expectations)
 * **Feature Discovery and Governance**
@@ -249,4 +249,4 @@ Thanks goes to these incredible people:
 
 <a href="https://github.com/feast-dev/feast/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=feast-dev/feast" />
-</a>
+</a>

docs/getting-started/architecture/push-vs-pull-model.md

@@ -25,4 +25,4 @@ Implicit in the Push model are decisions about _how_ and _when_ to push feature
 
 From a developer's perspective, there are three ways to push feature values to the online store with different tradeoffs.
 
-They are discussed further in the [Write Patterns](getting-started/architecture/write-patterns.md) section.
+They are discussed further in the [Write Patterns](write-patterns.md) section.

docs/getting-started/architecture/write-patterns.md

@@ -1,6 +1,6 @@
 # Writing Data to Feast
 
-Feast uses a [Push Model](getting-started/architecture/push-vs-pull-model.md) to push features to the online store.
+Feast uses a [Push Model](push-vs-pull-model.md) to push features to the online store.
 
 This has two important consequences: (1) communication patterns between the Data Producer (i.e., the client) and Feast (i.e,. the server) and (2) feature computation and 
 _feature value_ write patterns to Feast's online store.

pyproject.toml

@@ -57,6 +57,7 @@ azure = [
 cassandra = ["cassandra-driver>=3.24.0,<4"]
 couchbase = ["couchbase==4.3.2"]
 delta = ["deltalake"]
+docling = ["docling>=2.23.0"]
 duckdb = ["ibis-framework[duckdb]>=9.0.0,<10"]
 elasticsearch = ["elasticsearch>=8.13.0"]
 faiss = ["faiss-cpu>=1.7.0,<2"]
@@ -94,6 +95,7 @@ opentelemetry = ["prometheus_client", "psutil"]
 spark = ["pyspark>=3.0.0,<4"]
 trino = ["trino>=0.305.0,<0.400.0", "regex"]
 postgres = ["psycopg[binary,pool]>=3.0.0,<4"]
+pytorch = ["torch>=2.2.2", "torchvision>=0.17.2"]
 qdrant = ["qdrant-client>=1.12.0"]
 redis = [
     "redis>=4.2.2,<5",
@@ -148,8 +150,9 @@ ci = [
     "types-setuptools",
     "types-tabulate",
     "virtualenv<20.24.2",
-    "feast[aws, azure, cassandra, couchbase, delta, duckdb, elasticsearch, faiss, gcp, ge, go, grpcio, hazelcast, hbase, ibis, ikv, k8s, milvus, mssql, mysql, opentelemetry, spark, trino, postgres, qdrant, redis, singlestore, snowflake, sqlite_vec]"
+    "feast[aws, azure, cassandra, couchbase, delta, docling, duckdb, elasticsearch, faiss, gcp, ge, go, grpcio, hazelcast, hbase, ibis, ikv, k8s, milvus, mssql, mysql, opentelemetry, spark, trino, postgres, pytorch, qdrant, redis, singlestore, snowflake, sqlite_vec]"
 ]
+nlp = ["feast[docling, milvus, pytorch]"]
 dev = ["feast[ci]"]
 docs = ["feast[ci]"]