langchain/docs/extras/expression_language/cookbook/prompt_llm_parser.ipynb

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   "source": [
    "---\n",
    "sidebar_position: 0\n",
    "title: Prompt + LLM\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a434f2b-9405-468c-9dfd-254d456b57a6",
   "metadata": {},
   "source": [
    "The most common and valuable composition is taking:\n",
    "\n",
    "``PromptTemplate`` / ``ChatPromptTemplate`` -> ``LLM`` / ``ChatModel`` -> ``OutputParser``\n",
    "\n",
    "Almost any other chains you build will use this building block."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93aa2c87",
   "metadata": {},
   "source": [
    "## PromptTemplate + LLM\n",
    "\n",
    "The simplest composition is just combing a prompt and model to create a chain that takes user input, adds it to a prompt, passes it to a model, and returns the raw model input.\n",
    "\n",
    "Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "466b65b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\n",
    "model = ChatOpenAI()\n",
    "chain = prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3d0a6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb9ef50",
   "metadata": {},
   "source": [
    "Often times we want to attach kwargs that'll be passed to each model call. Here's a few examples of that:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b1d8f88",
   "metadata": {},
   "source": [
    "### Attaching Stop Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "562a06bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | model.bind(stop=[\"\\n\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43f5d04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='Why did the bear never wear shoes?', additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3eaf88a",
   "metadata": {},
   "source": [
    "### Attaching Function Call information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f94b71b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "functions = [\n",
    "    {\n",
    "      \"name\": \"joke\",\n",
    "      \"description\": \"A joke\",\n",
    "      \"parameters\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          \"setup\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The setup for the joke\"\n",
    "          },\n",
    "          \"punchline\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The punchline for the joke\"\n",
    "          }\n",
    "        },\n",
    "        \"required\": [\"setup\", \"punchline\"]\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    "chain = prompt | model.bind(function_call= {\"name\": \"joke\"}, functions= functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "decf7710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'joke', 'arguments': '{\\n  \"setup\": \"Why don\\'t bears wear shoes?\",\\n  \"punchline\": \"Because they have bear feet!\"\\n}'}}, example=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"}, config={})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9098c5ed",
   "metadata": {},
   "source": [
    "## PromptTemplate + LLM + OutputParser\n",
    "\n",
    "We can also add in an output parser to easily trasform the raw LLM/ChatModel output into a more workable format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc194c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.output_parser import StrOutputParser\n",
    "\n",
    "chain = prompt | model | StrOutputParser()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77acf448",
   "metadata": {},
   "source": [
    "Notice that this now returns a string - a much more workable format for downstream tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3d69a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01864e5",
   "metadata": {},
   "source": [
    "### Functions Output Parser\n",
    "\n",
    "When you specify the function to return, you may just want to parse that directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad0dd88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
    "\n",
    "chain = (\n",
    "    prompt \n",
    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
    "    | JsonOutputFunctionsParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e7aa8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'setup': \"Why don't bears like fast food?\",\n",
       " 'punchline': \"Because they can't catch it!\"}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d4aa1a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser\n",
    "\n",
    "chain = (\n",
    "    prompt \n",
    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b6df9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes?\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"foo\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023fbccb-ef7d-489e-a9ba-f98e17283d51",
   "metadata": {},
   "source": [
    "## Simplifying input\n",
    "\n",
    "To make invocation even simpler, we can add a `RunnableMap` to take care of creating the prompt input dict for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9601c0f0-71f9-4bd4-a672-7bd04084b018",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnableMap, RunnablePassthrough\n",
    "\n",
    "map_ = RunnableMap({\"foo\": RunnablePassthrough()})\n",
    "chain = (\n",
    "    map_ \n",
    "    | prompt\n",
    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ec4f154-fda5-4847-9220-41aa902fdc33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes?\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke(\"bears\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def00bfe-0f83-4805-8c8f-8a53f99fa8ea",
   "metadata": {},
   "source": [
    "Since we're composing our map with another Runnable, we can even use some syntactic sugar and just use a dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7bf3846a-02ee-41a3-ba1b-a708827d4f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = (\n",
    "    {\"foo\": RunnablePassthrough()} \n",
    "    | prompt\n",
    "    | model.bind(function_call= {\"name\": \"joke\"}, functions= functions) \n",
    "    | JsonKeyOutputFunctionsParser(key_name=\"setup\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e566d6a1-538d-4cb5-a210-a63e082e4c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears like fast food?\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke(\"bears\")"
   ]
  }
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