--- a
+++ b/aiagents4pharma/talk2biomodels/api/ols.py
@@ -0,0 +1,72 @@
+"""
+This module contains the API for fetching ols database
+"""
+from typing import List, Dict
+import requests
+
+def fetch_from_ols(term: str) -> str:
+    """
+    Fetch the label for a single term from OLS.
+
+    Args:
+        term (str): The term in the format "ONTOLOGY:TERM_ID".
+
+    Returns:
+        str: The label for the term or an error message.
+    """
+    try:
+        ontology, _ = term.split(":")
+        base_url = f"https://www.ebi.ac.uk/ols4/api/ontologies/{ontology.lower()}/terms"
+        params = {"obo_id": term}
+        response = requests.get(
+            base_url,
+            params=params,
+            headers={"Accept": "application/json"},
+            timeout=10
+        )
+        response.raise_for_status()
+        data = response.json()
+        label = '-'
+        # Extract and return the label
+        if "_embedded" in data and "terms" in data["_embedded"] \
+             and len(data["_embedded"]["terms"]) > 0:
+            label = data["_embedded"]["terms"][0].get("label", "Label not found")
+        return label
+    except (requests.exceptions.RequestException, KeyError, IndexError) as e:
+        return f"Error: {str(e)}"
+
+def fetch_ols_labels(terms: List[str]) -> Dict[str, str]:
+    """
+    Fetch labels for multiple terms from OLS.
+
+    Args:
+        terms (List[str]): A list of terms in the format "ONTOLOGY:TERM_ID".
+
+    Returns:
+        Dict[str, str]: A mapping of term IDs to their labels or error messages.
+    """
+    results = {}
+    for term in terms:
+        results[term] = fetch_from_ols(term)
+    return results
+
+def search_ols_labels(data: List[Dict[str, str]]) -> Dict[str, Dict[str, str]]:
+    """
+    Fetch OLS annotations grouped by ontology type.
+
+    Args:
+        data (List[Dict[str, str]]): A list of dictionaries containing 'Id' and 'Database'.
+
+    Returns:
+        Dict[str, Dict[str, str]]: A mapping of ontology type to term labels.
+    """
+    grouped_data = {}
+    for entry in data:
+        ontology = entry["Database"].lower()
+        grouped_data.setdefault(ontology, []).append(entry["Id"])
+
+    results = {}
+    for ontology, terms in grouped_data.items():
+        results[ontology] = fetch_ols_labels(terms)
+
+    return results