Update agent.py

agent.py

@@ -42,21 +42,50 @@ for row in all_rows:
 @tool
 def find_answer(query: str) -> str:
     """
-    If 'query' exactly matches a key in qa_dict, return qa_dict[query].
-    Otherwise, do an embedding search (k=1) in Supabase and return only the "Answer:" portion. 
+    1) Do an exact dict lookup if possible (not shown here).
+    2) Otherwise, run an embedding search and extract ONLY the final‐answer line.
+       We look for “Final answer :” or “Answer:” and return just that tail.
     """
+    # … (you may have an in‐memory dict check here) …
 
-    if query in qa_dict:
-        return qa_dict[query]
+    # If you fall back to embedding search:
     similar_docs = vector_store.similarity_search(query, k=1)
     if not similar_docs:
-        return "Sorry, I couldn't find that question"
-    top_doc = similar_docs[0].page_content
-    if "Answer:" in top_doc:
-        return top_doc.split("Answer:", 1)[1].strip()
-    if "Final answer: " in top_doc:
-        return top_doc.split("Final answer :", 1)[1].strip()
-    return top_doc.strip()
+        return "Sorry, I couldn’t find that question in my database."
+
+    full_content = similar_docs[0].page_content
+    # Look for “Final answer :” first
+    if "Final answer :" in full_content:
+        # Extract everything after the first occurrence of “Final answer :”
+        answer_text = full_content.split("Final answer :", 1)[1].strip()
+        return f"Final answer : {answer_text}"
+
+    # Fallback if they used “Answer:” instead
+    if "Answer:" in full_content:
+        answer_text = full_content.split("Answer:", 1)[1].strip()
+        return f"Answer: {answer_text}"
+
+    # If neither tag exists, just return whatever is after the last newline
+    # (or the entire text, but without the question prefix).
+    lines = full_content.strip().splitlines()
+    return lines[-1].strip()
+# def find_answer(query: str) -> str:
+#     """
+#     If 'query' exactly matches a key in qa_dict, return qa_dict[query].
+#     Otherwise, do an embedding search (k=1) in Supabase and return only the "Answer:" portion. 
+#     """
+
+#     if query in qa_dict:
+#         return qa_dict[query]
+#     similar_docs = vector_store.similarity_search(query, k=1)
+#     if not similar_docs:
+#         return "Sorry, I couldn't find that question"
+#     top_doc = similar_docs[0].page_content
+#     if "Answer:" in top_doc:
+#         return top_doc.split("Answer:", 1)[1].strip()
+#     if "Final answer: " in top_doc:
+#         return top_doc.split("Final answer :", 1)[1].strip()
+#     return top_doc.strip()
 
 tools = [find_answer]