add return_direct flag to tool (#537)

adds a return_direct flag to tools, which just returns the tool output
as the final output

docs/modules/agents/examples/custom_tools.ipynb

@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "36ed392e",
    "metadata": {},
    "outputs": [],
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "56ff7670",
    "metadata": {},
    "outputs": [],
@@ -94,7 +94,6 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "llm = OpenAI(temperature=0)\n",
     "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
    ]
   },
@@ -248,10 +247,86 @@
     "agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a8d95999",
+   "metadata": {},
+   "source": [
+    "## Using tools to return directly\n",
+    "\n",
+    "Often, it can be desirable to have a tool output returned directly to the user, if it's called. You can do this easily with LangChain by setting the `return_direct` flag for a tool to be True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c8f65640",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_math_chain = LLMMathChain(llm=llm)\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Calculator\",\n",
+    "        func=llm_math_chain.run,\n",
+    "        description=\"useful for when you need to answer questions about math\",\n",
+    "        return_direct=True\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4bd1c4bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52fe0594",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to calculate this\n",
+      "Action: Calculator\n",
+      "Action Input: 2**.12\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2599210498948732\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 1.2599210498948732'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats 2**.12\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3450512e",
+   "id": "7cc1b875",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -259,7 +334,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -273,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {