Update branch to latest version python 3.11 support only

llm-monitoring-main.ipynb

@@ -48,12 +48,24 @@
     "After creating the project, you enable model monitoring."
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import mlrun\n",
+    "from datasets import Dataset, load_dataset\n",
+    "import pandas as pd\n",
+    "from src.llm_as_a_judge import OpenAIJudge"
+   ],
+   "id": "2186eac3109c41eb"
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "6e99f06d",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Create the project:\n",
     "project = mlrun.get_or_create_project(\n",
@@ -65,14 +77,26 @@
     "    context=\"./src\",\n",
     ")\n",
     "\n",
+    "secrets = mlrun.set_env_from_file('env.env', return_dict=True)\n",
+    "project.set_secrets(secrets)"
+   ],
+   "outputs": []
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
     "# Enable model monitoring\n",
     "from src.model_monitoring_utils import enable_model_monitoring\n",
     "\n",
     "# If this project was running with MM enabled pre-1.8.0, disable the old model monitoring to update configurations\n",
     "project.disable_model_monitoring(delete_stream_function=True)\n",
     "\n",
-    "enable_model_monitoring(project=project, base_period=2)\n"
-   ]
+    "enable_model_monitoring(project=project, base_period=2)"
+   ],
+   "id": "78a9e4fe68462400"
   },
   {
    "cell_type": "markdown",
@@ -277,6 +301,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "OPENAI_MODEL = mlrun.get_secret_or_env(\"OPENAI_MODEL\")\n",
     "# Creating the OpenAI Judge\n",
     "judge = OpenAIJudge(\n",
     "    judge_type=\"custom-grading\",\n",
@@ -437,14 +462,11 @@
    "source": [
     "# Define application requirements\n",
     "requirements = ['openai==1.108.0',\n",
-    "'transformers==4.56.1',\n",
-    "'optimum==1.27.0',\n",
     "'deepeval==2.5.5',\n",
     "'llama-index==0.14.2',\n",
     "'llama-index-core==0.14.2',\n",
-    "]\n",
-    "if sys.version_info.major == 3 and sys.version_info.minor == 9:\n",
-    "    requirements += ['protobuf==3.20.3']"
+    "'langchain==0.2.17',\n",
+    "]"
    ]
   },
   {
@@ -456,6 +478,12 @@
     "\n"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "",
+   "id": "e31576a009f2ff93"
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -560,6 +588,8 @@
    },
    "outputs": [],
    "source": [
+    "from mlrun.features import Feature\n",
+    "\n",
     "# Log the model to the project:\n",
     "base_model = \"google-gemma-2b\"\n",
     "project.log_model(\n",
@@ -696,6 +726,7 @@
    },
    "outputs": [],
    "source": [
+    "from mlrun.model_monitoring.helpers import get_result_instance_fqn\n",
     "prj_alert_obj = get_result_instance_fqn(\n",
     "    ep_id, app_name=app_name, result_name=result_name\n",
     ")\n",
@@ -718,6 +749,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import mlrun.common.schemas.alert as alert_constants\n",
     "import mlrun.common.schemas.alert as alert_objects"
    ]
   },
@@ -904,7 +936,10 @@
     "from deepeval.metrics import (\n",
     "    AnswerRelevancyMetric,\n",
     "    HallucinationMetric,\n",
-    ")"
+    ")\n",
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = mlrun.get_secret_or_env(\"OPENAI_API_KEY\")\n",
+    "os.environ[\"OPENAI_BASE_URL\"]= mlrun.get_secret_or_env(\"OPENAI_API_BASE\")"
    ]
   },
   {
@@ -1072,7 +1107,7 @@
     "ret = project.run_function(\n",
     "    function=\"generate-ds\",\n",
     "    handler=\"generate_ds\",\n",
-    "    params={\"input_ds\": input_ds,\"hf_repo_id\":None},\n",
+    "    params={\"input_ds\": input_ds},\n",
     "    outputs=[\"new-train-ds\", \"dataset\"],\n",
     ")"
    ]

requirements.txt

@@ -5,9 +5,7 @@ openai==1.108.0
 transformers==4.56.1
 datasets==4.1.1
 sentencepiece==0.2.0
-deepeval==2.5.5
+deepeval==3.7.0
 pyarrow>=21.0.0
 pydantic>=2.0
-langchain==0.2.17
-
-
+langchain==0.2.17

src/deepeval_as_a_judge.py

@@ -42,38 +42,36 @@ def __init__(
         self.name = "deepeval-as-a-judge"
         self.metric_name = kwargs.pop("metric_name")
         os.environ["OPENAI_API_KEY"] = mlrun.get_secret_or_env("OPENAI_API_KEY")
-        os.environ["OPENAI_BASE_URL"]= mlrun.get_secret_or_env("OPENAI_API_BASE")
-
+        os.environ["OPENAI_BASE_URL"] = mlrun.get_secret_or_env("OPENAI_API_BASE")
 
     def judge(self, sample_df: pd.DataFrame) -> pd.DataFrame:
-
         result_df = pd.DataFrame(columns=["question", "answer", "score", "explanation"])
 
         correctness_metric = GEval(
             name="Correctness",
             criteria="Correctness - determine if the actual output is related to banking.",
             evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT]
         )
-        
+
         for i in range(len(sample_df)):
-            question, answer = sample_df.loc[i, "question"], sample_df.loc[i, "answer"]            
+            question, answer = sample_df.loc[i, "question"], sample_df.loc[i, "answer"]
             test_case = LLMTestCase(
                 input=question,
                 actual_output=answer
             )
             correctness_metric.measure(test_case)
-            
+
             correctness_metric.score, correctness_metric.reason
-            
+
             result_df.loc[i] = [
                 question,
                 answer,
                 correctness_metric.score,
                 correctness_metric.reason,
             ]
-            
+
         return result_df
-        
+
     def do_tracking(
         self,
         monitoring_context,

src/generate_ds.py

@@ -1,5 +1,4 @@
 import openai
-from datasets import Dataset
 from huggingface_hub import create_repo, login
 
 import mlrun
@@ -87,15 +86,4 @@ def generate_ds(context: MLClientCtx, input_ds: str, hf_repo_id:str =None):
     df.drop(inplace=True, columns=["answer", "explanation"])
     context.log_dataset("new-train-ds", df)
     context.logger.info("Dataframe logged")
-    if hf_repo_id:
-        # Upload the dataset to HuggingFace
-        hf_dataset = Dataset.from_pandas(df)
-        login(token=hf_token)
-
-        # Create a new repository on the Hugging Face Hub
-        create_repo(hf_repo_id, repo_type="dataset", exist_ok=True)
-
-        # Push the dataset to the Hub
-        hf_dataset.push_to_hub(hf_repo_id)
-        context.log_result("dataset", hf_repo_id)
-        context.logger.info("Dataset uploaded to HF")
+