added setup for environment and secrets

notebooks/5_ip_creating_deployments.ipynb

@@ -260,7 +260,7 @@
    "outputs": [],
    "source": [
     "query_model_deployment = (\n",
-    "    hopsworks_integration.two_tower_serving.HopsworksQueryModel.deploy(project=project)\n",
+    "    hopsworks_integration.two_tower_serving.HopsworksQueryModel.deploy()\n",
     ")"
    ]
   },

notebooks/7_ip_creating_llm_ranking_deployment.ipynb

@@ -248,9 +248,7 @@
     }
    ],
    "source": [
-    "ranking_deployment = hopsworks_integration.llm_ranking_serving.HopsworksLLMRankingModel.deploy(\n",
-    "    project=project\n",
-    ")"
+    "ranking_deployment = hopsworks_integration.llm_ranking_serving.HopsworksLLMRankingModel.deploy()"
    ]
   },
   {

recsys/config.py

@@ -43,7 +43,7 @@ class Settings(BaseSettings):
     RANKING_EARLY_STOPPING_ROUNDS: int = 5
 
     # Inference
-    RANKING_MODEL_TYPE: str = "llmranking"
+    RANKING_MODEL_TYPE: str = "ranking"
 
 
 settings = Settings()

recsys/hopsworks_integration/feature_store.py

@@ -14,41 +14,12 @@ def get_feature_store():
         project = hopsworks.login(
             api_key_value=settings.HOPSWORKS_API_KEY.get_secret_value()
         )
-        _init_secrets()
     else:
         logger.info("Login to Hopsworks using cached API key.")
         project = hopsworks.login()
     return project, project.get_feature_store()
 
 
-def get_secrets_api():
-    connection = hopsworks.connection(host="c.app.hopsworks.ai",
-                                      hostname_verification=False,
-                                      port=443,
-                                      api_key_value=settings.HOPSWORKS_API_KEY.get_secret_value()
-                                      )
-    return connection.get_secrets_api()
-
-
-def _init_secrets():
-    secrets_api = get_secrets_api()
-    secrets = secrets_api.get_secrets()
-    existing_secret_keys = [secret.name for secret in secrets]
-    # Create the OPENAI_API_KEY secret if it doesn't exist
-    if "OPENAI_API_KEY" not in existing_secret_keys:
-        secrets_api.create_secret(
-            "OPENAI_API_KEY",
-            settings.OPENAI_API_KEY.get_secret_value(),
-            project="recommandersystem"
-        )
-    # Create the RANKING_MODEL_TYPE secret if it doesn't exist
-    if "RANKING_MODEL_TYPE" not in existing_secret_keys:
-        secrets_api.create_secret(
-            "RANKING_MODEL_TYPE",
-            settings.RANKING_MODEL_TYPE,
-            project="recommandersystem"
-        )
-
 ########################
 #### Feature Groups ####
 ########################

recsys/hopsworks_integration/llm_ranker/requirements.txt

@@ -0,0 +1,2 @@
+langchain==0.2.6
+langchain-openai==0.1.14

recsys/hopsworks_integration/llm_ranking_serving.py

@@ -1,7 +1,7 @@
 import os
+import hopsworks
 
 from hsml.transformer import Transformer
-
 from recsys.config import settings
 
 
@@ -18,7 +18,12 @@ def register(cls, mr):
         ranking_model.save(local_model_path)
 
     @classmethod
-    def deploy(cls, project):
+    def deploy(cls):
+        # Prepare secrets used in the deployment
+        cls._prepare_secrets()
+
+        project = hopsworks.login()
+        cls._prepare_environment(project)
         mr = project.get_model_registry()
         dataset_api = project.get_dataset_api()
 
@@ -71,3 +76,42 @@ def deploy(cls, project):
         )
 
         return ranking_deployment
+
+    @classmethod
+    def _prepare_environment(cls, project):
+        # Upload requirements file to Hopsworks
+        dataset_api = project.get_dataset_api()
+
+        requirements_path = dataset_api.upload(
+            str(settings.RECSYS_DIR / "hopsworks_integration" / "llm_ranker" / "requirements.txt"),
+            "Resources",
+            overwrite=True,
+        )
+
+        # Install the extra requirements in the Python environment on Hopsworks
+        env_api = project.get_environment_api()
+        env = env_api.get_environments()[0]
+        print(env)
+        env.install_requirements(requirements_path)
+    @classmethod
+    def _prepare_secrets(cls):
+        connection = hopsworks.connection(host="c.app.hopsworks.ai",
+                                          hostname_verification=False,
+                                          port=443,
+                                          api_key_value=settings.HOPSWORKS_API_KEY.get_secret_value()
+                                          )
+        if not settings.OPENAI_API_KEY:
+            raise ValueError(
+                "Missing required secret: 'OPENAI_API_KEY'. Please ensure it is set in the .env file or config.py "
+                "settings.")
+
+        secrets_api = connection.get_secrets_api()
+        secrets = secrets_api.get_secrets()
+        existing_secret_keys = [secret.name for secret in secrets]
+        # Create the OPENAI_API_KEY secret if it doesn't exist
+        if "OPENAI_API_KEY" not in existing_secret_keys:
+            secrets_api.create_secret(
+                "OPENAI_API_KEY",
+                settings.OPENAI_API_KEY.get_secret_value(),
+                project=settings.HOPSWORKS_PROJECT
+            )

recsys/hopsworks_integration/two_tower_serving.py

@@ -1,5 +1,5 @@
 import os
-
+import hopsworks
 from loguru import logger
 import tensorflow as tf
 from hsml.model_schema import ModelSchema
@@ -89,7 +89,11 @@ def register(self, mr, query_df, emb_dim) -> None:
         mr_query_model.save(local_model_path)  # Path to save the model
 
     @classmethod
-    def deploy(cls, project):
+    def deploy(cls):
+        # Prepare secrets used in the deployment
+        cls._prepare_secrets()
+
+        project = hopsworks.login()
         mr = project.get_model_registry()
         dataset_api = project.get_dataset_api()
 
@@ -128,6 +132,30 @@ def deploy(cls, project):
 
         return query_model_deployment
 
+    @classmethod
+    def _prepare_secrets(cls):
+        connection = hopsworks.connection(host="c.app.hopsworks.ai",
+                                          hostname_verification=False,
+                                          port=443,
+                                          api_key_value=settings.HOPSWORKS_API_KEY.get_secret_value()
+                                          )
+        if not settings.RANKING_MODEL_TYPE:
+            raise ValueError(
+                "Missing required secret: 'RANKING_MODEL_TYPE'. Please ensure it is set in the .env file or config.py "
+                "settings.")
+
+        secrets_api = connection.get_secrets_api()
+        secrets = secrets_api.get_secrets()
+        existing_secret_keys = [secret.name for secret in secrets]
+
+        # Create the RANKING_MODEL_TYPE secret if it doesn't exist
+        if "RANKING_MODEL_TYPE" not in existing_secret_keys:
+            secrets_api.create_secret(
+                "RANKING_MODEL_TYPE",
+                settings.RANKING_MODEL_TYPE,
+                project=settings.HOPSWORKS_PROJECT
+            )
+
 
 class QueryModelModule(tf.Module):
     def __init__(self, model: QueryTower) -> None:

recsys/inference/llm_ranking_predictor.py

@@ -1,4 +1,5 @@
 import logging
+import hopsworks
 from langchain import PromptTemplate, LLMChain
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel, ValidationError, Field
@@ -46,13 +47,18 @@ def __init__(self):
                                 "graphical_appearance_name", "colour_group_name", "perceived_colour_value_name",
                                 "perceived_colour_master_name", "department_name", "index_name", "index_group_name",
                                 "section_name", "garment_group_name"]
+        self._retrieve_secrets()
         self.llm = self._build_lang_chain()
 
+    def _retrieve_secrets(self):
+        secrets_api = hopsworks.connection().get_secrets_api()
+        self.openai_api_key = secrets_api.get_secret("OPENAI_API_KEY")
+
     def predict(self, inputs):
         logging.info(f"✅ Inputs: {inputs}")
 
-        # Extract ranking features and article IDs from the inputs
-        # limit to 20 candidates because of OPENAI token requests limitations
+        # Extract ranking features and article IDs from the inputs limit to 20 candidates because otherwise the
+        # inference time is over 60 seconds and the predict endpoint closes the socket
         features = inputs[0].pop("ranking_features")[:20]
         article_ids = inputs[0].pop("article_ids")[:20]
 
@@ -114,11 +120,10 @@ def _postprocess_output(self, output):
 
     def _build_lang_chain(self):
 
-        # todo get from secrets api
         model = ChatOpenAI(
             model_name='gpt-4o-mini-2024-07-18',
             temperature=0.7,
-            openai_api_key="OPENAI_KEY",
+            openai_api_key=self.openai_api_key,
         )
         prompt = PromptTemplate(
             input_variables=self.input_variables,

recsys/inference/query_transformer.py

@@ -1,9 +1,9 @@
+import logging
 from datetime import datetime
 
 import hopsworks
-import numpy as np
-
 import nest_asyncio
+
 nest_asyncio.apply()
 import pandas as pd
 
@@ -12,6 +12,7 @@ def __init__(self) -> None:
         # Connect to the Hopsworks
         project = hopsworks.connection().get_project()
         ms = project.get_model_serving()
+        self._retrieve_secrets()
 
         # Retrieve the 'customers' feature view
         fs = project.get_feature_store()
@@ -25,8 +26,16 @@ def __init__(self) -> None:
         self.ranking_fv.init_batch_scoring(1)
 
         # Retrieve the ranking deployment
-        # todo retrieve from secrets ranking or llmranking according to ranker used
-        self.ranking_server = ms.get_deployment("ranking")
+        self.ranking_server = ms.get_deployment(self.ranking_model_type)
+
+    def _retrieve_secrets(self):
+        secrets_api = hopsworks.connection().get_secrets_api()
+        try:
+            self.ranking_model_type = secrets_api.get_secret("RANKING_MODEL_TYPE")
+        except Exception as e:
+            logging.error(e)
+            logging.error("Could not retrieve secret RANKING_MODEL_TYPE, defaulting to ranker")
+            self.ranking_model_type = "ranking"
 
     def preprocess(self, inputs):
         # Check if the input data contains a key named "instances"