Update app.py

app.py

@@ -190,26 +190,33 @@ load_or_create_faiss()
 @tool
 def retrieve_documents(query: str) -> str:
     """
-    Retrieve a clean, text-only answer by matching documents using FAISS.
-    Returns only the combined snippets (no source metadata).
+    🧠 Use this tool when the answer is likely found in a document, article, magazine, or written content.
+
+    💡 Ideal for questions that involve:
+    - What someone said, did, or thinks
+    - Quotes, opinions, event mentions
+    - Details that would appear in a news article, magazine, or report
+
+    ❌ Do NOT use for email addresses or CRM-like participant info (use Zoho tool instead)
+
+    This tool uses a FAISS vector store and RetrievalQA to find the most relevant chunks and generate a context-based answer.
     """
     if not vector_store:
-        return "I'm sorry, the document system is currently unavailable."
-
-    def clean(text):
-        text = re.sub(r'\[([^\]]+)\]\((https?://[^\)]+)\)', r'\1', text)
-        text = re.sub(r'https?://\S+', '', text)
-        return text.strip()
+        return "The document system is currently unavailable."
 
     try:
-        docs = vector_store.similarity_search(query, k=5)
-        if not docs:
-            return "No relevant documents found."
-        combined = "\n\n".join(clean(d.page_content) for d in docs)
-        return combined
+        retriever = vector_store.as_retriever(search_kwargs={"k": 5})
+        qa = RetrievalQA.from_chain_type(
+            llm=ChatOpenAI(model="gpt-4", temperature=0),
+            retriever=retriever,
+            return_source_documents=False,
+            chain_type="stuff"
+        )
+        return qa.run(query)
+
     except Exception as e:
-        logging.error(f"Document retrieval failed: {e}")
-        return "There was an error retrieving documents."
+        logging.error(f"Document QA error: {e}")
+        return "There was an error fetching the answer from documents."
 @tool
 def fetch_youtube_videos(query: str) -> list:
     """