{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "2XVP2VXIL1"
      },
      "source": [
        "# Chains\n",
        "\n",
        "Chaining LLMs with each other or with other experts.\n",
        "\n",
        "## Getting Started\n",
        "\n",
        "- Using the simple LLM chain\n",
        "- Creating sequential chains\n",
        "- Creating a custom chain\n",
        "\n",
        "### Why Use Chains ?\n",
        "\n",
        "- combine multiple components together\n",
        "- ex: take user input, format with PromptTemplate, pass formatted text to LLM.\n",
        "\n",
        "## Query an LLM with LLMChain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "DPRWRo3fl7"
      },
      "source": [
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms import OpenAI\n",
        "import pprint as pp\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",
        "        )"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "tOpTb9idHh"
      },
      "source": [
        "We can now create a simple chain that takes user input format it and pass to LLM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "QXu2N1dEEC"
      },
      "source": [
        "from langchain.chains import LLMChain\n",
        "chain = LLMChain(llm=llm, prompt=prompt, output_key='company_name')\n",
        "\n",
        "# run the chain only specifying input variables\n",
        "print(chain.run(\"hand crafted handbags\"))\n",
        "\n",
        "# NOTE: we pass data to the run of the entry chain (see sequence under)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "\n\nUrban Crafts Co.\n"
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "Kv6bj1l9I3"
      },
      "source": [
        "## Combining chains with SequentialChain\n",
        "\n",
        "Chains that execute their links in predefined order.\n",
        "\n",
        "- SimpleSequentialChain: simplest form, each step has a single input/output. \n",
        "Output of one step is input to next.\n",
        "- SequentialChain: More advanced, multiple inputs/outputs.\n",
        "\n",
        "\n",
        "Following tutorial uses SimpleSequentialChain and SequentialChain, each chains output is input to the next one.\n",
        "This sequential chain will:\n",
        "    1. create company name for a product. We just use LLMChain for that\n",
        "    2. Create a catchphrase for the product. We will use a new LLMChain for the catchphrase, as show below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "BMZLsdY9VP"
      },
      "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, output_key='catchphrase')"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "epQHxmeWCP"
      },
      "source": [
        "We now combine the two chains to create company name and catch phrase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "SHwDHjVCxb"
      },
      "source": [
        "from langchain.chains import SimpleSequentialChain, SequentialChain"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "lKgp9HR0VX"
      },
      "source": [
        "full_chain = SimpleSequentialChain(\n",
        "        chains=[chain, chain_two], verbose=True,\n",
        "        )\n",
        "\n",
        "print(full_chain.run(\"hand crafted handbags\"))"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "RiYcYwJhdC"
      },
      "source": [
        "---\n",
        "\n",
        "In the third prompt we create an small advertisement with the title and the product description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "RhnqOumOtX"
      },
      "source": [
        "ad_template = \"\"\"Create a small advertisement destined for reddit. \n",
        "The advertisement is for a company with the following details:\n",
        "\n",
        "name: {company_name}\n",
        "product: {product}\n",
        "catchphrase: {catchphrase}\n",
        "\n",
        "advertisement:\n",
        "\"\"\"\n",
        "ad_prompt = PromptTemplate(\n",
        "        input_variables=[\"product\", \"company_name\", \"catchphrase\"],\n",
        "        template=ad_template,\n",
        "        )"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "MsQnieyxgL"
      },
      "source": [
        "#Connet the three chains together\n",
        "\n",
        "ad_chain = LLMChain(llm=llm, prompt=ad_prompt, output_key='advertisement')"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "4PYfwOxTlq"
      },
      "source": [
        "final_chain = SequentialChain(\n",
        "        chains=[chain, chain_two, ad_chain],\n",
        "        input_variables=['product'],\n",
        "        output_variables=['advertisement'],\n",
        "        verbose=True\n",
        "        )\n",
        "\n",
        "ad = final_chain.run('Professional Cat Cuddler')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "\n\n\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "2akm8eB1EV"
      },
      "source": [
        "print(ad)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Are you in need of a little indulgence? Then come to Purr-fect Pampering! Our professional cat cuddler will provide you with the ultimate relaxation experience. We guarantee that after a session with us, you'll be feeling more purr-fect than ever! Treat yourself to the luxurious indulgence of Purr-fect Pampering!\n"
        }
      ],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "1iT7gBMABZ"
      },
      "source": [
        "## Creating a custom chain\n",
        "\n",
        "Example: create a custom chain that concats output of 2 LLMChain\n",
        "\n",
        "Steps:\n",
        "    1. Subclass Chain class\n",
        "    2. Fill out `input_keys` and `output_keys`\n",
        "    3. add the `_call` method that shows how to execute chain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "OUXv7kGtDH"
      },
      "source": [
        "from langchain.chains import LLMChain\n",
        "from langchain.chains.base import Chain\n",
        "\n",
        "from typing import Dict, List\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_inputs_vars = set(self.chain_1.input_keys).union(\n",
        "                        set(self.chain_2.input_keys))\n",
        "        return list(all_inputs_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}"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jukit_cell_id": "MUOMbKovF6"
      },
      "source": [
        "Running the custom chain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "kBfPU3rB6L"
      },
      "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",
        "\n",
        "concat_output = concat_chain.run('leather handbags')\n",
        "print(f'Concatenated output:\\n{concat_output}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Concatenated output:\n\n\nLeather Luxury Boutique.\n\n\"Handcrafted Leather: The Perfect Accent for Any Look.\"\n"
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "metadata": {
        "jukit_cell_id": "9CdH3GtsmW"
      },
      "source": [],
      "outputs": [],
      "execution_count": null
    }
  ],
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    "anaconda-cloud": {},
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