From efc0a191300916db1be458ad569c68815aae5cd2 Mon Sep 17 00:00:00 2001
From: Gal Topper <gal.topper@gmail.com>
Date: Tue, 16 Apr 2024 10:46:40 +0800
Subject: [PATCH] Update readme with links to mlrun docs (#513)

Replacing outdated and partial information.
---
 README.md | 104 +++---------------------------------------------------
 1 file changed, 4 insertions(+), 100 deletions(-)

diff --git a/README.md b/README.md
index 3467b31f..a98bb3f2 100644
--- a/README.md
+++ b/README.md
@@ -2,104 +2,8 @@
 
 [![CI](https://github.com/mlrun/storey/workflows/CI/badge.svg)](https://github.com/mlrun/storey/actions?query=workflow%3ACI)
 
-Storey is an asynchronous streaming library, for real time event processing and feature extraction.
+Storey is an asynchronous streaming library, for real time event processing and feature extraction. It's a component
+of mlrun.
 
-#### In This Document
-
-- [API Walkthrough](#api-walkthrough)
-- [Usage Examples](#examples)
-
-&#x25B6; For more information, see the [Storey Python package documentation](https://storey.readthedocs.io).
-
-<a id="api-walkthrough"></a>
-## API Walkthrough
-A Storey flow consist of steps linked together by the `build_flow` function, each doing it's designated work.
-
-### Supported Steps
-#### Input Steps
-* `SyncEmitSource` 
-* `AsyncEmitSource` 
-* `CSVSource`  
-* `ParquetSource` 
-* `DataframeSource`  
-
-#### Processing Steps
-* `Filter` 
-* `Map` 
-* `FlatMap`
-* `MapWithState`
-* `Batch(max_events, timeout)` - Batches events. This step emits a batch every max_events events, or when timeout seconds have passed since the first event in the batch was received.
-* `Choice`  
-* `JoinWithV3IOTable` 
-* `SendToHttp` 
-* `AggregateByKey(aggregations,cache, key=None, emit_policy=EmitEveryEvent(), augmentation_fn=None)` - This step aggregates the data into the cache object provided for later persistence, and outputs an event enriched with the requested aggregation features.
-* `QueryByKey(features, cache, key=None, augmentation_fn=None, aliases=None)` - Similar to to `AggregateByKey`, but this step is for serving only and does not aggregate the event.
-* `NoSqlTarget(table)` - Persists the data in `table` to its associated storage by key.
-* `Extend`
-* `JoinWithTable`  
-
-#### Output Steps
-* `Complete`  
-* `Reduce` 
-* `StreamTarget` 
-* `CSVTarget`
-* `ReduceToDataFrame`
-* `TSDBTarget`
-* `ParquetTarget`     
-
-
-<a id="examples"></a>
-## Usage Examples
-
-### Using Aggregates
-The following example reads user data, creates features using Storey's aggregates, persists the data to V3IO and emits events containing the features to a V3IO Stream for further processing.
-
-```python
-from storey import build_flow, SyncEmitSource, Table, V3ioDriver, AggregateByKey, FieldAggregator, NoSqlTarget
-from storey.dtypes import SlidingWindows
-
-v3io_web_api = "https://webapi.change-me.com"
-v3io_acceess_key = "1284ne83-i262-46m6-9a23-810n41f169ea"
-table_object = Table("/projects/my_features", V3ioDriver(v3io_web_api, v3io_acceess_key))
-
-def enrich(event, state):
-    if "first_activity" not in state:
-        state["first_activity"] = event.time
-    event.body["time_since_activity"] = (event.body["time"] - state["first_activity"]).seconds
-    state["last_event"] = event.time
-    event.body["total_activities"] = state["total_activities"] = state.get("total_activities", 0) + 1
-    return event, state
-
-controller = build_flow([
-    SyncEmitSource(),
-    MapWithState(table_object, enrich, group_by_key=True, full_event=True),
-    AggregateByKey([FieldAggregator("number_of_clicks", "click", ["count"],
-                                    SlidingWindows(["1h","2h", "24h"], "10m")),
-                    FieldAggregator("purchases", "purchase_amount", ["avg", "min", "max"],
-                                    SlidingWindows(["1h","2h", "24h"], "10m")),
-                    FieldAggregator("failed_activities", "activity", ["count"],
-                                    SlidingWindows(["1h"], "10m"),
-                                    aggr_filter=lambda element: element["activity_status"] == "fail"))],
-                   table_object,
-                   time_field="time"),
-    NoSqlTarget(table_object),
-    StreamTarget(V3ioDriver(v3io_web_api, v3io_acceess_key), "features_stream")
-]).run()
-```
-
-We can also create a serving function, which sole purpose is to read data from the feature store and emit it further
-
-```python
-controller = build_flow([
-    SyncEmitSource(),
-    QueryAggregationByKey([FieldAggregator("number_of_clicks", "click", ["count"],
-                                           SlidingWindows(["1h","2h", "24h"], "10m")),
-                           FieldAggregator("purchases", "purchase_amount", ["avg", "min", "max"],
-                                           SlidingWindows(["1h","2h", "24h"], "10m")),
-                           FieldAggregator("failed_activities", "activity", ["count"],
-                                           SlidingWindows(["1h"], "10m"),
-                                           aggr_filter=lambda element: element["activity_status"] == "fail"))],
-                           table_object,
-                           time_field="time")
-]).run()
-```
+&#x25B6; For more information, see [mlrun documentation](https://docs.mlrun.org/en/stable/), and the page on [storey
+transformations](https://docs.mlrun.org/en/latest/api/storey.transformations.html) in particular.