langchain/docs/extras/guides/expression_language/interface.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a9acd2e",
   "metadata": {},
   "source": [
    "# Interface\n",
    "\n",
    "In an effort to make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.Runnable.html#langchain.schema.runnable.Runnable) protocol that most components implement. This is a standard interface with a few different methods, which makes it easy to define custom chains as well as making it possible to invoke them in a standard way. The standard interface exposed includes:\n",
    "\n",
    "- `stream`: stream back chunks of the response\n",
    "- `invoke`: call the chain on an input\n",
    "- `batch`: call the chain on a list of inputs\n",
    "\n",
    "These also have corresponding async methods:\n",
    "\n",
    "- `astream`: stream back chunks of the response async\n",
    "- `ainvoke`: call the chain on an input async\n",
    "- `abatch`: call the chain on a list of inputs async\n",
    "\n",
    "The type of the input varies by component. For a prompt it is a dictionary, for a retriever it is a single string, for a model either a single string, a list of chat messages, or a PromptValue.\n",
    "\n",
    "The output type also varies by component. For an LLM it is a string, for a ChatModel it's a ChatMessage, for a prompt it's a PromptValue, for a retriever it's a list of documents.\n",
    "\n",
    "Let's take a look at these methods! To do so, we'll create a super simple PromptTemplate + ChatModel chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "466b65b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chat_models import ChatOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c634ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ChatOpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1850a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56d0669f",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf2b2b2",
   "metadata": {},
   "source": [
    "## Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bea9639d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sure, here's a bear-themed joke for you:\n",
      "\n",
      "Why don't bears wear shoes?\n",
      "\n",
      "Because they have bear feet!"
     ]
    }
   ],
   "source": [
    "for s in chain.stream({\"topic\": \"bears\"}):\n",
    "    print(s.content, end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf1c782",
   "metadata": {},
   "source": [
    "## Invoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "470e483f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they already have bear feet!\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"topic\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f0c279",
   "metadata": {},
   "source": [
    "## Batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9685de67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[AIMessage(content=\"Why don't bears ever wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
       " AIMessage(content=\"Why don't cats play poker in the wild?\\n\\nToo many cheetahs!\", additional_kwargs={}, example=False)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b960cbfe",
   "metadata": {},
   "source": [
    "## Async Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea35eee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why don't bears wear shoes?\n",
      "\n",
      "Because they have bear feet!"
     ]
    }
   ],
   "source": [
    "async for s in chain.astream({\"topic\": \"bears\"}):\n",
    "    print(s.content, end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cb3324",
   "metadata": {},
   "source": [
    "## Async Invoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef8c9b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"Sure, here you go:\\n\\nWhy don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "await chain.ainvoke({\"topic\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da288d5",
   "metadata": {},
   "source": [
    "## Async Batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eba2a103",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "await chain.abatch([{\"topic\": \"bears\"}])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}