langchain/docs/docs/expression_language/how_to/functions.ipynb

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    "# Run arbitrary functions\n",
    "\n",
    "You can use arbitrary functions in the pipeline\n",
    "\n",
    "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bb221b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnableLambda\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from operator import itemgetter\n",
    "\n",
    "def length_function(text):\n",
    "    return len(text)\n",
    "\n",
    "def _multiple_length_function(text1, text2):\n",
    "    return len(text1) * len(text2)\n",
    "\n",
    "def multiple_length_function(_dict):\n",
    "    return _multiple_length_function(_dict[\"text1\"], _dict[\"text2\"])\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(\"what is {a} + {b}\")\n",
    "model = ChatOpenAI()\n",
    "\n",
    "chain1 = prompt | model\n",
    "\n",
    "chain = {\n",
    "    \"a\": itemgetter(\"foo\") | RunnableLambda(length_function),\n",
    "    \"b\": {\"text1\": itemgetter(\"foo\"), \"text2\": itemgetter(\"bar\")} | RunnableLambda(multiple_length_function)\n",
    "} | prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5488ec85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='3 + 9 equals 12.', additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bar\", \"bar\": \"gah\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4728ddd9-914d-42ce-ae9b-72c9ce8ec940",
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   "source": [
    "## Accepting a Runnable Config\n",
    "\n",
    "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.config.RunnableConfig.html?highlight=runnableconfig#langchain.schema.runnable.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80b3b5f6-5d58-44b9-807e-cce9a46bf49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnableConfig\n",
    "from langchain.schema.output_parser import StrOutputParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff0daf0c-49dd-4d21-9772-e5fa133c5f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def parse_or_fix(text: str, config: RunnableConfig):\n",
    "    fixing_chain = (\n",
    "        ChatPromptTemplate.from_template(\n",
    "            \"Fix the following text:\\n\\n```text\\n{input}\\n```\\nError: {error}\"\n",
    "            \" Don't narrate, just respond with the fixed data.\"\n",
    "        )\n",
    "        | ChatOpenAI()\n",
    "        | StrOutputParser()\n",
    "    )\n",
    "    for _ in range(3):\n",
    "        try:\n",
    "            return json.loads(text)\n",
    "        except Exception as e:\n",
    "            text = fixing_chain.invoke({\"input\": text, \"error\": e}, config)\n",
    "    return \"Failed to parse\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a5e709e-9d75-48c7-bb9c-503251990505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokens Used: 65\n",
      "\tPrompt Tokens: 56\n",
      "\tCompletion Tokens: 9\n",
      "Successful Requests: 1\n",
      "Total Cost (USD): $0.00010200000000000001\n"
     ]
    }
   ],
   "source": [
    "from langchain.callbacks import get_openai_callback\n",
    "\n",
    "with get_openai_callback() as cb:\n",
    "    RunnableLambda(parse_or_fix).invoke(\"{foo: bar}\", {\"tags\": [\"my-tag\"], \"callbacks\": [cb]})\n",
    "    print(cb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f55c38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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