Add copyright info and Pylance fixes

Author: brnil6

ai/generative-ai-service/hr-goal-alignment/.gitignore

@@ -1,7 +1,8 @@
 **/__pycache__/
 venv/
+files/venv/
 **/config.py
 .DS_Store
 data/
 *.log
-*.txt
+*.txt

ai/generative-ai-service/hr-goal-alignment/files/Org_Chart.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import streamlit as st
 from urllib.parse import quote, unquote
 
@@ -99,7 +100,7 @@ def create_org_chart():
 """
 
 # Display the chart with HTML component
-st.components.v1.html(html_chart, height=520, scrolling=True)
+st.components.v1.html(html_chart, height=520, scrolling=True) # type: ignore
 
 # Show details section for selected employee
 # ------------------------------------------------------------
@@ -181,5 +182,5 @@ def chip_style(val):
                 else "background-color:#f8d7da;font-weight:bold"
             )
 
-        styled = goals_pretty.style.applymap(chip_style, subset=[LABEL_COL])
+        styled = goals_pretty.style.applymap(chip_style, subset=[LABEL_COL]) # type: ignore
         st.dataframe(styled, use_container_width=True, hide_index=True)

ai/generative-ai-service/hr-goal-alignment/files/app.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import streamlit as st
 from pathlib import Path
 
@@ -29,4 +30,4 @@
 
 
 pg = st.navigation(pages)
-pg.run()
+pg.run()

ai/generative-ai-service/hr-goal-alignment/files/config_template.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 # config_template.py
 
 # === OCI Configuration ===

ai/generative-ai-service/hr-goal-alignment/files/course_vector_utils.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 from typing import List, Dict, Optional
 from langchain_community.vectorstores import OracleVS
 import os
@@ -12,6 +13,7 @@
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import OCIGenAIEmbeddings
 from langchain_core.documents import Document
+from langchain_community.vectorstores.utils import DistanceStrategy
 
 # Use the project's config file
 import config
@@ -60,7 +62,7 @@ def _initialize_vector_store(self) -> OracleVS:
             client=self.db_conn, # Use the connection passed in __init__
             embedding_function=self.embeddings, # Use the embeddings initialized in __init__
             table_name=config.VECTOR_TABLE_NAME, # Use table name from project config
-            distance_strategy="COSINE"
+            distance_strategy=DistanceStrategy.COSINE
         )
 
     # LLM initialization removed.
@@ -167,5 +169,3 @@ def similarity_search(self, query: str, k: int = 5) -> List[Document]:
         except Exception as e:
             logger.error(f"Error during similarity search: {e}", exc_info=True)
             return []
-
-

ai/generative-ai-service/hr-goal-alignment/files/data_ingestion_courses.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import logging
 import sys
 import oracledb

ai/generative-ai-service/hr-goal-alignment/files/gen_ai_service/inference.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import json
 import logging
 import sys
@@ -65,7 +66,7 @@ def recommend_courses(profile, feedback) -> dict:
     except Exception as e:
         logger.error(f"Error during recommended_courses execution: {e}")
 
-    return response
+    return response # type: ignore
 
 
 
@@ -81,7 +82,7 @@ def classify_smart_goal(goal_description) -> dict:
     except Exception as e:
         logger.error(f"Error during recommended_courses execution: {e}")
 
-    return json.loads(response.content)
+    return json.loads(response.content) # type: ignore
 
 
 prompt_sys = """

ai/generative-ai-service/hr-goal-alignment/files/goal_alignment_backend.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import oracledb
 import config
 import json
@@ -14,7 +15,7 @@
     model_kwargs={"temperature": 0.1, "max_tokens": 500},
 )
 
-def query_llm(prompt_template, inputs):
+def query_llm(prompt_template, inputs) -> str:
     """Formats prompt and calls the Oracle LLM"""
     if not isinstance(prompt_template, PromptTemplate):
         raise TypeError(" query_llm expected a PromptTemplate object.")
@@ -28,7 +29,7 @@ def query_llm(prompt_template, inputs):
     chain = RunnableSequence(prompt_template | llm)
     response = chain.invoke(inputs)
 
-    return response.content if isinstance(response, AIMessage) else response
+    return response.content if isinstance(response, AIMessage) else str(response) # type: ignore
 
 # --- Database Data Loading Functions ---
 
@@ -522,6 +523,3 @@ def update_employee_goal_objective(connection, employee_id, new_objective):
         print("Error updating goal objective:")
         print(error)
         return "Error updating goal objective."
-    
-
-

ai/generative-ai-service/hr-goal-alignment/files/org_chart_backend.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import oracledb
 import pandas as pd
 
@@ -8,13 +9,14 @@
 connection = oracledb.connect(
             **config.CONNECT_ARGS_VECTOR
         )
-def mapping_all_employees() -> dict[str, list[str]]:
+def mapping_all_employees() -> tuple[dict[str, list[str]], pd.DataFrame]:
     query = (
         "SELECT employee_id, name, role, manager_id "
         "FROM Employees"
     )
+    df_db = pd.DataFrame() # Initialize df_db
     try:
-        df_db = pd.read_sql(query, connection)
+        df_db = pd.read_sql(query, connection) # type: ignore
 
     except Exception as err:
         print(f"Query failed: {err}")
@@ -53,17 +55,20 @@ def check_smart_goal(df_row: pd.Series) -> str:
     return goal_smart.get("classification", "N/A")
 
 
-def fetch_goals_from_emp(df, emp_data) -> list[str]:
-    df_db = None
+def fetch_goals_from_emp(df, emp_data) -> pd.DataFrame:
+    df_db = pd.DataFrame() # Initialize as empty DataFrame
     try:
         emp_id = search_employee(df, emp_data)
-        query = f"SELECT title, objective, metrics, timeline FROM Goals WHERE employee_id = '{emp_id}'"
-        df_db = pd.read_sql(query, connection)
+        if emp_id: # Only proceed if emp_id is found
+            query = f"SELECT title, objective, metrics, timeline FROM Goals WHERE employee_id = '{emp_id}'"
+            df_db = pd.read_sql(query, connection) # type: ignore
 
     except oracledb.Error as err:
         print(f"Oracle connection error: {err}")
+        # df_db remains an empty DataFrame if an error occurs
 
-    df_db["smart"] = df_db.apply(check_smart_goal, axis=1)
+    if not df_db.empty: # Check if DataFrame is not empty before applying
+        df_db["smart"] = df_db.apply(check_smart_goal, axis=1)
 
     return df_db
 
@@ -103,4 +108,4 @@ def check_goal_alignment(df, emp_data, manager_data):
     else:
         vertical = None
 
-    return vertical, horizontal
+    return vertical, horizontal

ai/generative-ai-service/hr-goal-alignment/files/pages/course_recommendation_chatbot.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2025 Oracle and/or its affiliates.
 import streamlit as st
 import logging
 import oracledb