Utilize Polars.DataFrame for performance in ModelbitComponent

.pre-commit-config.yaml

@@ -62,6 +62,7 @@ repos:
           - types-tqdm
           - nest-asyncio
           - aiohttp
+          - polars
         exclude: "^tests/"
 
   # Check for spelling

examples/feature_store_main.py

@@ -5,6 +5,7 @@
 import typer
 from pydantic import BaseModel
 
+from wyvern import Identifier
 from wyvern.components.api_route_component import APIRouteComponent
 from wyvern.components.features.feature_store import (
     FeatureStoreRetrievalRequest,
@@ -33,13 +34,24 @@ async def execute(
         self, input: FeatureStoreRetrievalRequest, **kwargs
     ) -> FeatureStoreResponse:
         logger.info(f"Executing input {input}")
-        feature_map = await feature_store_retrieval_component.execute(input)
-
+        feature_df = await feature_store_retrieval_component.execute(input)
+        feature_dicts = feature_df.df.to_dicts()
+        feature_data: Dict[str, FeatureData] = {
+            str(feature_dict["IDENTIFIER"]): FeatureData(
+                identifier=Identifier(
+                    identifier_type=feature_dict["IDENTIFIER"].split("::")[0],
+                    identifier=feature_dict["IDENTIFIER"].split("::")[1],
+                ),
+                features={
+                    feature_name: feature_value
+                    for feature_name, feature_value in feature_dict.items()
+                    if feature_name != "IDENTIFIER"
+                },
+            )
+            for feature_dict in feature_dicts
+        }
         return FeatureStoreResponse(
-            feature_data={
-                identifier.identifier: feature_map.feature_map[identifier]
-                for identifier in feature_map.feature_map.keys()
-            },
+            feature_data=feature_data,
         )
 
 

examples/real_time_features_main.py

@@ -337,15 +337,27 @@ async def execute(
         )
 
         time_start = time()
-        feature_map = await self.feature_retrieval_pipeline.execute(request)
+        feature_df = await self.feature_retrieval_pipeline.execute(request)
         logger.info(f"operation feature_retrieval took:{time()-time_start:2.4f} sec")
         profiler.stop()
         profiler.print()
+        feature_dicts = feature_df.df.to_dicts()
+        feature_data: Dict[str, FeatureData] = {
+            str(feature_dict["IDENTIFIER"]): FeatureData(
+                identifier=Identifier(
+                    identifier_type=feature_dict["IDENTIFIER"].split("::")[0],
+                    identifier=feature_dict["IDENTIFIER"].split("::")[1],
+                ),
+                features={
+                    feature_name: feature_value
+                    for feature_name, feature_value in feature_dict.items()
+                    if feature_name != "IDENTIFIER"
+                },
+            )
+            for feature_dict in feature_dicts
+        }
         return FeatureStoreResponse(
-            feature_data={
-                str(identifier): feature_map.feature_map[identifier]
-                for identifier in feature_map.feature_map.keys()
-            },
+            feature_data=feature_data,
         )
 
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "wyvern-ai"
-version = "0.0.26"
+version = "0.0.27"
 description = ""
 authors = ["Wyvern AI <[email protected]>"]
 readme = "README.md"
@@ -33,6 +33,7 @@ aiohttp = {extras = ["speedups"], version = "^3.8.5"}
 requests = "^2.31.0"
 platformdirs = "^3.8"
 posthog = "^3.0.2"
+polars = "^0.19.6"
 
 
 [tool.poetry.group.dev.dependencies]

tests/components/business_logic/test_pinning_business_logic.py

@@ -13,7 +13,7 @@
     PinningBusinessLogicComponent,
 )
 from wyvern.entities.candidate_entities import ScoredCandidate
-from wyvern.entities.feature_entities import FeatureMap
+from wyvern.entities.feature_entities import FeatureDataFrame
 from wyvern.entities.identifier_entities import ProductEntity
 from wyvern.entities.request import BaseWyvernRequest
 from wyvern.wyvern_request import WyvernRequest
@@ -65,7 +65,7 @@ def __init__(self):
             headers={},
             entity_store={},
             events=[],
-            feature_map=FeatureMap(feature_map={}),
+            feature_df=FeatureDataFrame(),
             model_output_map={},
         ),
     )