Chaining.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax9TJMm2dksa",
        "outputId": "6c06177b-7c35-4d34-ea91-3e06ce321a0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gemini API Key:··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import getpass\n",
        "os.environ['GOOGLE_API_KEY'] = getpass.getpass('Gemini API Key:')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-google-genai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKmJboWQejYs",
        "outputId": "5ee72349-8f57-4ad9-8945-c5b0692f9237"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: orjson, jsonpointer, h11, requests-toolbelt, jsonpatch, httpcore, httpx, langsmith, langchain-core, langchain-google-genai\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-core-0.3.12 langchain-google-genai-2.0.1 langsmith-0.1.137 orjson-3.10.10 requests-toolbelt-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-community langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84VMJ8i7e3Vf",
        "outputId": "dad732c3-779d-4c0e-ec49-9cd814db5bc8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-community\n",
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            "Installing collected packages: python-dotenv, mypy-extensions, marshmallow, typing-inspect, pydantic-settings, dataclasses-json, langchain-text-splitters, langchain, langchain-community\n",
            "Successfully installed dataclasses-json-0.6.7 langchain-0.3.4 langchain-community-0.3.3 langchain-text-splitters-0.3.0 marshmallow-3.23.0 mypy-extensions-1.0.0 pydantic-settings-2.6.0 python-dotenv-1.0.1 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "llm = ChatGoogleGenerativeAI(model=\"gemini-pro\",\n",
        "                 temperature=0.7, top_p=0.85)"
      ],
      "metadata": {
        "id": "bn8kJABKejgH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.prompts import ChatPromptTemplate\n",
        "from langchain.chains import LLMChain"
      ],
      "metadata": {
        "id": "xCTQ2UefexPM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = ChatPromptTemplate.from_template(\n",
        "    \"What is the best name to describe \\\n",
        "    a company that makes {product}?\"\n",
        ")"
      ],
      "metadata": {
        "id": "h_xZU_oue1D7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = LLMChain(llm=llm, prompt=prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZn5yAqMgHqS",
        "outputId": "45448a08-7792-4134-d0e3-2e861f30cf2a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-726db833e4be>:1: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
            "  chain = LLMChain(llm=llm, prompt=prompt)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product = \"Queen Size Sheet Set\"\n",
        "chain.run(product)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "mSXyLdPAgHxp",
        "outputId": "ea912804-c282-4493-ecf2-11642c0e595c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-a419ed0609fa>:2: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
            "  chain.run(product)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'* Royal Rest\\n* Majestic Slumber\\n* Serene Sleep\\n* Dreamy Dimensions\\n* Tranquil Textiles\\n* Cozy Corner\\n* Luxe Linens\\n* Soft Sensations\\n* Comfort Cloud\\n* Pillowtop Perfection'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Sequential Chain"
      ],
      "metadata": {
        "id": "SnAfe5l_hAMv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import SimpleSequentialChain\n",
        "first_prompt = ChatPromptTemplate.from_template(\n",
        "    \"What is the best name to describe \\\n",
        "    a company that makes {product}?\"\n",
        ")\n",
        "chain_one = LLMChain(llm=llm, prompt=first_prompt)\n",
        "\n",
        "second_prompt = ChatPromptTemplate.from_template(\n",
        "    \"Write a 20 words description for the following \\\n",
        "    company:{company_name}\"\n",
        ")\n",
        "\n",
        "chain_two = LLMChain(llm=llm, prompt=second_prompt)\n",
        "\n",
        "overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two],\n",
        "                                             verbose=True\n",
        "                                            )\n",
        "\n",
        "overall_simple_chain.run(product)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "OiJoHBUPg5AW",
        "outputId": "2e53c7e4-6181-4e78-d52a-e655a80852bd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
            "\u001b[36;1m\u001b[1;3m* Royal Slumber\n",
            "* Majestic Linens\n",
            "* Imperial Bedding\n",
            "* Queen's Court Sheets\n",
            "* Grandiose Sheets\n",
            "* Regal Threads\n",
            "* Sovereign Sleep\n",
            "* Tranquil Haven\n",
            "* Serene Sanctuary\n",
            "* Dreamweaver Bedding\u001b[0m\n",
            "\u001b[33;1m\u001b[1;3mIndulge in the ultimate slumber experience with our luxurious bedding collections, fit for royalty and designed to provide a serene and tranquil haven.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Indulge in the ultimate slumber experience with our luxurious bedding collections, fit for royalty and designed to provide a serene and tranquil haven.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sequential Chain"
      ],
      "metadata": {
        "id": "ZQJVpk4soVQm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import SequentialChain"
      ],
      "metadata": {
        "id": "1GxxyEsfhKhc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt template 1: translate to english\n",
        "first_prompt = ChatPromptTemplate.from_template(\n",
        "    \"Translate the following review to english:\"\n",
        "    \"\\n\\n{Review}\"\n",
        ")\n",
        "# chain 1: input= Review and output= English_Review\n",
        "chain_one = LLMChain(llm=llm, prompt=first_prompt,\n",
        "                     output_key=\"English_Review\"\n",
        "                    )\n",
        "\n",
        "\n",
        "second_prompt = ChatPromptTemplate.from_template(\n",
        "    \"Can you summarize the following review in 1 sentence:\"\n",
        "    \"\\n\\n{English_Review}\"\n",
        ")\n",
        "# chain 2: input= English_Review and output= summary\n",
        "chain_two = LLMChain(llm=llm, prompt=second_prompt,\n",
        "                     output_key=\"summary\"\n",
        "                    )\n",
        "\n",
        "\n",
        "# prompt template 3: translate to english\n",
        "third_prompt = ChatPromptTemplate.from_template(\n",
        "    \"What language is the following review:\\n\\n{Review}\"\n",
        ")\n",
        "# chain 3: input= Review and output= language\n",
        "chain_three = LLMChain(llm=llm, prompt=third_prompt,\n",
        "                       output_key=\"language\"\n",
        "                      )\n",
        "\n",
        "\n",
        "\n",
        "# prompt template 4: follow up message\n",
        "fourth_prompt = ChatPromptTemplate.from_template(\n",
        "    \"Write a follow up response to the following \"\n",
        "    \"summary in the specified language:\"\n",
        "    \"\\n\\nSummary: {summary}\\n\\nLanguage: {language}\"\n",
        ")\n",
        "# chain 4: input= summary, language and output= followup_message\n",
        "chain_four = LLMChain(llm=llm, prompt=fourth_prompt,\n",
        "                      output_key=\"followup_message\"\n",
        "                     )\n",
        "\n",
        "\n",
        "# overall_chain: input= Review\n",
        "# and output= English_Review,summary, followup_message\n",
        "overall_chain = SequentialChain(\n",
        "    chains=[chain_one, chain_two, chain_three, chain_four],\n",
        "    input_variables=[\"Review\"],\n",
        "    output_variables=[\"English_Review\", \"summary\",\"followup_message\"],\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "IlAOQSFIol4W"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "X8xtwajyo5oN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/Data.csv')"
      ],
      "metadata": {
        "id": "J8L7hOb4oyIp"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review = df.Review[5]\n",
        "overall_chain(review)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2UU2-loom74",
        "outputId": "2c098cf5-1a89-40d8-c42f-c02d4897b5e4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-b19509cc8fc9>:2: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
            "  overall_chain(review)\n"
          ]
        },
        {
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Review': \"Je trouve le goût médiocre. La mousse ne tient pas, c'est bizarre. J'achète les mêmes dans le commerce et le goût est bien meilleur...\\nVieux lot ou contrefaçon !?\",\n",
              " 'English_Review': \"I find the taste mediocre. The foam does not hold, it's weird. I buy the same ones in the store and the taste is much better...\\nOld batch or counterfeit!?\",\n",
              " 'summary': \"The reviewer found the taste and foam quality of the purchased coffee inferior to that of the same product purchased in stores, raising concerns about the batch's freshness or authenticity.\",\n",
              " 'followup_message': \"Le critique a trouvé que le goût et la qualité de la mousse du café acheté étaient inférieurs à ceux du même produit acheté en magasin, ce qui soulève des inquiétudes quant à la fraîcheur ou à l'authenticité du lot.\"}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Router Chain"
      ],
      "metadata": {
        "id": "_5l47eixpAoP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "physics_template = \"\"\"You are a very smart physics professor. \\\n",
        "You are great at answering questions about physics in a concise\\\n",
        "and easy to understand manner. \\\n",
        "When you don't know the answer to a question you admit\\\n",
        "that you don't know.\n",
        "\n",
        "Here is a question:\n",
        "{input}\"\"\"\n"
      ],
      "metadata": {
        "id": "JuNVqlXaoqmz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "math_template = \"\"\"You are a very good mathematician. \\\n",
        "You are great at answering math questions. \\\n",
        "You are so good because you are able to break down \\\n",
        "hard problems into their component parts,\n",
        "answer the component parts, and then put them together\\\n",
        "to answer the broader question.\n",
        "\n",
        "Here is a question:\n",
        "{input}\"\"\""
      ],
      "metadata": {
        "id": "DAqPWXoXpUoR"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_template = \"\"\"You are a very good historian. \\\n",
        "You have an excellent knowledge of and understanding of people,\\\n",
        "events and contexts from a range of historical periods. \\\n",
        "You have the ability to think, reflect, debate, discuss and \\\n",
        "evaluate the past. You have a respect for historical evidence\\\n",
        "and the ability to make use of it to support your explanations \\\n",
        "and judgements.\n",
        "\n",
        "Here is a question:\n",
        "{input}\"\"\""
      ],
      "metadata": {
        "id": "alrnSsxlpX_9"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "computerscience_template = \"\"\" You are a successful computer scientist.\\\n",
        "You have a passion for creativity, collaboration,\\\n",
        "forward-thinking, confidence, strong problem-solving capabilities,\\\n",
        "understanding of theories and algorithms, and excellent communication \\\n",
        "skills. You are great at answering coding questions. \\\n",
        "You are so good because you know how to solve a problem by \\\n",
        "describing the solution in imperative steps \\\n",
        "that a machine can easily interpret and you know how to \\\n",
        "choose a solution that has a good balance between \\\n",
        "time complexity and space complexity.\n",
        "\n",
        "Here is a question:\n",
        "{input}\"\"\""
      ],
      "metadata": {
        "id": "uNou7erdpc8z"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_infos = [\n",
        "    {\n",
        "        \"name\": \"physics\",\n",
        "        \"description\": \"Good for answering questions about physics\",\n",
        "        \"prompt_template\": physics_template\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"math\",\n",
        "        \"description\": \"Good for answering math questions\",\n",
        "        \"prompt_template\": math_template\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"History\",\n",
        "        \"description\": \"Good for answering history questions\",\n",
        "        \"prompt_template\": history_template\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"computer science\",\n",
        "        \"description\": \"Good for answering computer science questions\",\n",
        "        \"prompt_template\": computerscience_template\n",
        "    }\n",
        "]"
      ],
      "metadata": {
        "id": "EU5W7fYppfrp"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.router import MultiPromptChain\n",
        "from langchain.chains.router.llm_router import LLMRouterChain,RouterOutputParser\n",
        "from langchain.prompts import PromptTemplate"
      ],
      "metadata": {
        "id": "Kwj0iOdRpjxW"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "destination_chains = {}\n",
        "for p_info in prompt_infos:\n",
        "    name = p_info[\"name\"]\n",
        "    prompt_template = p_info[\"prompt_template\"]\n",
        "    prompt = ChatPromptTemplate.from_template(template=prompt_template)\n",
        "    chain = LLMChain(llm=llm, prompt=prompt)\n",
        "    destination_chains[name] = chain\n",
        "\n",
        "destinations = [f\"{p['name']}: {p['description']}\" for p in prompt_infos]\n",
        "destinations_str = \"\\n\".join(destinations)"
      ],
      "metadata": {
        "id": "KshdlqBQppMv"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "default_prompt = ChatPromptTemplate.from_template(\"{input}\")\n",
        "default_chain = LLMChain(llm=llm, prompt=default_prompt)\n",
        "\n",
        "MULTI_PROMPT_ROUTER_TEMPLATE = \"\"\" Given a raw text input to a\n",
        "language model select the model prompt best suited for the input.\n",
        "You will be given the names of the available prompts and a\n",
        "description of what the prompt is best suited for.\n",
        "You may also revise the original input if you think that revising\n",
        "it will ultimately lead to a better response from the language model.\n",
        "\n",
        "<< FORMATTING >>\n",
        "Return a markdown code snippet with a JSON object formatted to look like:\n",
        "json\n",
        "{{{{\n",
        "    \"destination\": string \\ name of the prompt to use or \"DEFAULT\"\n",
        "    \"next_inputs\": string \\ a potentially modified version of the original input\n",
        "}}}}\n",
        "REMEMBER: \"destination\" MUST be one of the candidate prompt \\\n",
        "names specified below OR it can be \"DEFAULT\" if the input is not\\\n",
        "well suited for any of the candidate prompts.\n",
        "REMEMBER: \"next_inputs\" can just be the original input \\\n",
        "if you don't think any modifications are needed.\n",
        "\n",
        "<< CANDIDATE PROMPTS >>\n",
        "{destinations}\n",
        "\n",
        "<< INPUT >>\n",
        "{{input}}\n",
        "\n",
        "<< OUTPUT (remember to include the ```json)>>\"\"\""
      ],
      "metadata": {
        "id": "51focCn_ps7N"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(\n",
        "    destinations=destinations_str\n",
        ")\n",
        "router_prompt = PromptTemplate(\n",
        "    template=router_template,\n",
        "    input_variables=[\"input\"],\n",
        "    output_parser=RouterOutputParser(),\n",
        ")\n",
        "\n",
        "router_chain = LLMRouterChain.from_llm(llm, router_prompt)"
      ],
      "metadata": {
        "id": "ijCgxMgJpw6r"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = MultiPromptChain(router_chain=router_chain,\n",
        "                         destination_chains=destination_chains,\n",
        "                         default_chain=default_chain, verbose=True\n",
        "                        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTeikB55qP7-",
        "outputId": "3f160595-ab2f-4fc6-d8d5-5e362efb78fb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-ae9cc652db54>:1: LangChainDeprecationWarning: Use RunnableLambda to select from multiple prompt templates. See example in API reference: https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html\n",
            "  chain = MultiPromptChain(router_chain=router_chain,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.run(\"What is black body radiation?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "AuIFZ1FdrWlq",
        "outputId": "0cd325f3-bc98-42dd-970b-907d497f112f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
            "physics: {'input': 'What is black body radiation?'}\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Black body radiation is the electromagnetic radiation emitted by a black body, which is an idealized object that absorbs all incident radiation and reflects none. The spectrum of black body radiation depends only on the temperature of the black body and is independent of its shape or composition.\\n\\nThe peak wavelength of black body radiation is inversely proportional to the temperature of the black body. For example, the Sun, which has a surface temperature of about 5,778 K, emits most of its radiation in the visible light spectrum. In contrast, a human body, which has a surface temperature of about 310 K, emits most of its radiation in the infrared spectrum.\\n\\nBlack body radiation is a fundamental concept in physics and has applications in many areas, including astrophysics, optics, and thermodynamics.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.run(\"what is 2 + 2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "QbZX6LvKri4v",
        "outputId": "2e77021e-3915-455b-e4c2-d2eb60578569"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
            "math: {'input': 'what is 2 + 2'}\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2 + 2 is a simple addition problem. The component parts are the numbers 2 and 2. To answer the component parts, we simply add them together. 2 + 2 = 4. Therefore, the answer to the broader question is 4.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.run(\"Why does every cell in our body contain DNA?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "Dc3cDl_MrZN-",
        "outputId": "fabd2bc2-39d9-496c-cec9-ef800ee2e337"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
            "biology: {'input': 'Why does every cell in our body contain DNA?'}"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Received invalid destination chain name 'biology'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-7fec47bc1aa9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Why does every cell in our body contain DNA?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m             return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[\n\u001b[0m\u001b[1;32m    607\u001b[0m                 \u001b[0m_output_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m             ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    387\u001b[0m         }\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             outputs = (\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/router/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    107\u001b[0m                 \u001b[0;34mf\"Received invalid destination chain name '{route.destination}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Received invalid destination chain name 'biology'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mf-a0ki7rfcx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}