langchain/docs/modules/chains/getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.\n",
    "\n",
    "In this tutorial, we will cover:\n",
    "- Using a simple LLM chain\n",
    "- Creating sequential chains\n",
    "- Creating a custom chain\n",
    "\n",
    "## Why do we need chains?\n",
    "\n",
    "Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick start: Using `LLMChain`\n",
    "\n",
    "The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.\n",
    "\n",
    "\n",
    "To use the `LLMChain`, first create a prompt template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "llm = OpenAI(temperature=0.9)\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"product\"],\n",
    "    template=\"What is a good name for a company that makes {product}?\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "SockSplash!\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import LLMChain\n",
    "chain = LLMChain(llm=llm, prompt=prompt)\n",
    "\n",
    "# Run the chain only specifying the input variable.\n",
    "print(chain.run(\"colorful socks\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use a chat model in an `LLMChain` as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rainbow Sox Co.\n"
     ]
    }
   ],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.prompts.chat import (\n",
    "    ChatPromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    ")\n",
    "human_message_prompt = HumanMessagePromptTemplate(\n",
    "        prompt=PromptTemplate(\n",
    "            template=\"What is a good name for a company that makes {product}?\",\n",
    "            input_variables=[\"product\"],\n",
    "        )\n",
    "    )\n",
    "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
    "chat = ChatOpenAI(temperature=0.9)\n",
    "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
    "print(chain.run(\"colorful socks\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different ways of calling chains\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adjective': 'corny',\n",
       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat = ChatOpenAI(temperature=0)\n",
    "prompt_template = \"Tell me a {adjective} joke\"\n",
    "llm_chain = LLMChain(\n",
    "    llm=chat,\n",
    "    prompt=PromptTemplate.from_template(prompt_template)\n",
    ")\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain(\"corny\", return_only_outputs=True)"
   ]
  },
  {
   "attachments": {},
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['text']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# llm_chain only has one output key, so we can use run\n",
    "llm_chain.output_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Why did the tomato turn red? Because it saw the salad dressing!'"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adjective': 'corny',\n",
       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
      ]
     },
     "execution_count": 8,
     "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.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add memory to chains\n",
    "\n",
    "`Chain` supports taking a `BaseMemory` object as its `memory` argument, allowing `Chain` object to persist data across multiple calls. In other words, it makes `Chain` a stateful object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The next four colors of a rainbow are green, blue, indigo, and violet.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "conversation = ConversationChain(\n",
    "    llm=chat,\n",
    "    memory=ConversationBufferMemory()\n",
    ")\n",
    "\n",
    "conversation.run(\"Answer briefly. What are the first 3 colors of a rainbow?\")\n",
    "# -> The first three colors of a rainbow are red, orange, and yellow.\n",
    "conversation.run(\"And the next 4?\")\n",
    "# -> The next four colors of a rainbow are green, blue, indigo, and violet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially, `BaseMemory` defines an interface of how `langchain` stores memory. It allows reading of stored data through `load_memory_variables` method and storing new data through `save_context` method. You can learn more about it in [Memory](../memory/getting_started.ipynb) section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Debug Chain\n",
    "\n",
    "It can be hard to debug `Chain` object solely from its output as most `Chain` objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Setting `verbose` to `True` will print out some internal states of the `Chain` object while it is being ran."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: What is ChatGPT?\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation = ConversationChain(\n",
    "    llm=chat,\n",
    "    memory=ConversationBufferMemory(),\n",
    "    verbose=True\n",
    ")\n",
    "conversation.run(\"What is ChatGPT?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine chains with the `SequentialChain`\n",
    "\n",
    "The next step after calling a language model is to make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest type of a sequential chain, where each step has a single input/output, and the output of one step is the input to the next.\n",
    "\n",
    "In this tutorial, our sequential chain will:\n",
    "1. First, create a company name for a product. We will reuse the `LLMChain` we'd previously initialized to create this company name.\n",
    "2. Then, create a catchphrase for the product. We will initialize a new `LLMChain` to create this catchphrase, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_prompt = PromptTemplate(\n",
    "    input_variables=[\"company_name\"],\n",
    "    template=\"Write a catchphrase for the following company: {company_name}\",\n",
    ")\n",
    "chain_two = LLMChain(llm=llm, prompt=second_prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can combine the two LLMChains, so that we can create a company name and a catchphrase in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3mRainbow Socks Co.\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3m\n",
      "\n",
      "\"Step into Color with Rainbow Socks!\"\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\"Step into Color with Rainbow Socks!\"\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import SimpleSequentialChain\n",
    "overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)\n",
    "\n",
    "# Run the chain specifying only the input variable for the first chain.\n",
    "catchphrase = overall_chain.run(\"colorful socks\")\n",
    "print(catchphrase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a custom chain with the `Chain` class\n",
    "\n",
    "LangChain provides many chains out of the box, but sometimes you may want to create a custom chain for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
    "\n",
    "In order to create a custom chain:\n",
    "1. Start by subclassing the `Chain` class,\n",
    "2. Fill out the `input_keys` and `output_keys` properties,\n",
    "3. Add the `_call` method that shows how to execute the chain.\n",
    "\n",
    "These steps are demonstrated in the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.chains.base import Chain\n",
    "\n",
    "from typing import Dict, List\n",
    "\n",
    "\n",
    "class ConcatenateChain(Chain):\n",
    "    chain_1: LLMChain\n",
    "    chain_2: LLMChain\n",
    "\n",
    "    @property\n",
    "    def input_keys(self) -> List[str]:\n",
    "        # Union of the input keys of the two chains.\n",
    "        all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))\n",
    "        return list(all_input_vars)\n",
    "\n",
    "    @property\n",
    "    def output_keys(self) -> List[str]:\n",
    "        return ['concat_output']\n",
    "\n",
    "    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:\n",
    "        output_1 = self.chain_1.run(inputs)\n",
    "        output_2 = self.chain_2.run(inputs)\n",
    "        return {'concat_output': output_1 + output_2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can try running the chain that we called.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenated output:\n",
      "\n",
      "\n",
      "Socktastic Colors.\n",
      "\n",
      "\"Put Some Color in Your Step!\"\n"
     ]
    }
   ],
   "source": [
    "prompt_1 = PromptTemplate(\n",
    "    input_variables=[\"product\"],\n",
    "    template=\"What is a good name for a company that makes {product}?\",\n",
    ")\n",
    "chain_1 = LLMChain(llm=llm, prompt=prompt_1)\n",
    "\n",
    "prompt_2 = PromptTemplate(\n",
    "    input_variables=[\"product\"],\n",
    "    template=\"What is a good slogan for a company that makes {product}?\",\n",
    ")\n",
    "chain_2 = LLMChain(llm=llm, prompt=prompt_2)\n",
    "\n",
    "concat_chain = ConcatenateChain(chain_1=chain_1, chain_2=chain_2)\n",
    "concat_output = concat_chain.run(\"colorful socks\")\n",
    "print(f\"Concatenated output:\\n{concat_output}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! For more details about how to do cool things with Chains, check out the [how-to guide](how_to_guides.rst) for chains."
   ]
  }
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