langchain/docs/extras/modules/chains/how_to/call_methods.ipynb

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   "source": [
    "# Different call methods\n",
    "\n",
    "All classes inherited from `Chain` offer a few ways of running chain logic. The most direct one is by using `__call__`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adjective': 'corny',\n",
       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat = ChatOpenAI(temperature=0)\n",
    "prompt_template = \"Tell me a {adjective} joke\"\n",
    "llm_chain = LLMChain(llm=chat, prompt=PromptTemplate.from_template(prompt_template))\n",
    "\n",
    "llm_chain(inputs={\"adjective\": \"corny\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `__call__` returns both the input and output key values. You can configure it to only return output key values by setting `return_only_outputs` to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "llm_chain(\"corny\", return_only_outputs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the `Chain` only outputs one output key (i.e. only has one element in its `output_keys`), you can  use `run` method. Note that `run` outputs a string instead of a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "['text']"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "# llm_chain only has one output key, so we can use run\n",
    "llm_chain.output_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Why did the tomato turn red? Because it saw the salad dressing!'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.run({\"adjective\": \"corny\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of one input key, you can input the string directly without specifying the input mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adjective': 'corny',\n",
       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These two are equivalent\n",
    "llm_chain.run({\"adjective\": \"corny\"})\n",
    "llm_chain.run(\"corny\")\n",
    "\n",
    "# These two are also equivalent\n",
    "llm_chain(\"corny\")\n",
    "llm_chain({\"adjective\": \"corny\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tips: You can easily integrate a `Chain` object as a `Tool` in your `Agent` via its `run` method. See an example [here](../agents/tools/custom_tools.html)."
   ]
  }
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