Update agent.py

agent.py

@@ -126,7 +126,50 @@ def wikipedia_fetcher(query:str)->str:
     doc = docs[0].page_content
     return doc
 
-tools = [read_file, wikipedia_fetcher, web_search, add, subtract, divide, multiply, modulus, power, square_root]
+@tool
+def query_csv_first_match(csv_file: str, filters: dict, column: str) -> str:
+    """
+    Returns the first value from `column` in CSV matching the filters.
+
+    Args:
+        csv_file (str): Path to CSV
+        filters (dict): Column-value filters, e.g. {"Year": 1983, "Country": "SU"}
+        column (str): Column to return value from
+
+    Returns:
+        str: First matching value
+    """
+    df = pd.read_csv(csv_file)
+    for key, val in filters.items():
+        if val is not None:
+            df = df[df[key] == val]
+    if not df.empty:
+        return str(df.iloc[0][column])
+    return "Unknown"
+
+@tool
+def query_csv_min(csv_file: str, column_min: str, return_column: str, tie_break_column: str = None) -> str:
+    """
+    Returns the value of `return_column` from the row with the minimum `column_min`.
+    If tie, use `tie_break_column` to select first alphabetically.
+
+    Args:
+        csv_file (str): Path to CSV
+        column_min (str): Column to find minimum
+        return_column (str): Column to return
+        tie_break_column (str, optional): Column to break tie alphabetically
+
+    Returns:
+        str: Result value
+    """
+    df = pd.read_csv(csv_file)
+    min_value = df[column_min].min()
+    candidates = df[df[column_min] == min_value]
+    if tie_break_column:
+        candidates = candidates.sort_values(tie_break_column)
+    return str(candidates.iloc[0][return_column])
+
+tools = [read_file, wikipedia_fetcher, web_search, add, subtract, divide, multiply, modulus, power, square_root, query_csv_first_match, query_csv_min]
 
 class AgentState(TypedDict):
     messages: Annotated[List[AnyMessage], add_messages]