AllInOneApp/langchain/docs/modules/chains/getting_started.ipynb

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    "# 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 the 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, format 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": [
    "## Query an LLM with the `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",
    "To use the `LLMChain`, first create a prompt template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Vibrancy Socks.\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": [
    "This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine chains with the `SequentialChain`\n",
    "\n",
    "The next step after calling a language model is 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 form of sequential chains, where each step has a singular 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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3m\n",
      "\n",
      "Cheerful Toes.\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3m\n",
      "\n",
      "\"Spread smiles from your toes!\"\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n",
      "\n",
      "\n",
      "\"Spread smiles from your toes!\"\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 chains 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": 5,
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenated output:\n",
      "\n",
      "\n",
      "Rainbow Socks Co.\n",
      "\n",
      "\"Step Into Colorful Comfort!\"\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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