LangChain_Pinecone_Serverless.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyML7UXVR5Vtg/YyvdwPkS+v",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sugarforever/LangChain-Tutorials/blob/main/LangChain_Pinecone_Serverless.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "py_LzY9P5qrs",
        "outputId": "56faba32-8eff-4487-badd-8c3e4271681d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.9/199.9 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.3/222.3 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m218.6/218.6 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.4/48.4 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install langchain-community==0.0.13 pinecone-client==3.0.0 openai -q -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "PINECONE_INDEX_NAME = 'serverless-example'\n",
        "\n",
        "import os\n",
        "\n",
        "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
        "os.environ['PINECONE_API_KEY'] = userdata.get('PINECONE_API_KEY')"
      ],
      "metadata": {
        "id": "0pmV2F7i686r"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.chat_models import ChatOpenAI\n",
        "from langchain_community.embeddings import OpenAIEmbeddings\n",
        "from langchain_community.vectorstores import Pinecone\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda\n",
        "from pinecone import Pinecone as PineconeClient"
      ],
      "metadata": {
        "id": "MKYDbcUm7YCe"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorstore = Pinecone.from_existing_index(index_name=PINECONE_INDEX_NAME, embedding=OpenAIEmbeddings())"
      ],
      "metadata": {
        "id": "CCEL7hig7mha"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O Optimism_and_StarkNet.pdf https://arxiv.org/pdf/2210.16610.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65ctMUEG9gcJ",
        "outputId": "9f8d791d-7919-4681-f093-74a9f9266969"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-01-17 19:49:34--  https://arxiv.org/pdf/2210.16610.pdf\n",
            "Resolving arxiv.org (arxiv.org)... 151.101.67.42, 151.101.3.42, 151.101.195.42, ...\n",
            "Connecting to arxiv.org (arxiv.org)|151.101.67.42|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 505763 (494K) [application/pdf]\n",
            "Saving to: ‘Optimism_and_StarkNet.pdf’\n",
            "\n",
            "Optimism_and_StarkN 100%[===================>] 493.91K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2024-01-17 19:49:35 (11.1 MB/s) - ‘Optimism_and_StarkNet.pdf’ saved [505763/505763]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U langchain pypdf tiktoken"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUYwwzxZ-RkL",
        "outputId": "f685f33f-8dbb-48d8-e798-387d0d4bb6c9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/2.0 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.5/2.0 MB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/2.0 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m1.6/2.0 MB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "\n",
        "loader = PyPDFLoader(\"Optimism_and_StarkNet.pdf\")\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "6rZwAeQR-WBQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=200,\n",
        "    length_function=len,\n",
        "    is_separator_regex=False,\n",
        ")\n",
        "split_docs = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "Eja9BBYR-vd0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorstore.add_documents(split_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaxTcv3o_XyM",
        "outputId": "29b49e66-24ec-4f1d-d8d8-b9d626b35a36"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
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              " '71e678a1-a70c-4f45-8d7e-a99d500644b4',\n",
              " 'e7754104-b701-45e9-9a5b-af1b959a442d',\n",
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              " '13dc2cac-975f-44a6-8f16-127a27e204b9',\n",
              " 'bbf5ff84-c251-4468-a1ca-80caa0add004',\n",
              " '7c5f187d-934a-49be-89e0-7d8653a45f29',\n",
              " '347f7d40-badd-4f89-9843-f8b9ddcf4a91',\n",
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              " '80bf999f-415c-46ed-8385-f73ea7f320a4',\n",
              " '45d1ee93-1626-4ee9-be10-03b05258d469',\n",
              " '5d689a9d-0248-41b0-a025-af93ffec870b',\n",
              " '07faff36-8f70-429f-a5c8-c40ac21a3d36',\n",
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              " '1f20d6f9-d80a-4458-b64a-75f8f5cb1847',\n",
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              " 'aa4fa059-7805-4cd0-be76-93b1b4be91b6',\n",
              " '91fae641-afe2-49e9-a1d3-3d5931ff90e9',\n",
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              " 'c6893f70-e42a-4739-88c5-9df8bc58ef19',\n",
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              " '79c9d6d3-3aef-4138-a20b-0e76903a027b',\n",
              " 'df6190bb-5da4-45d8-b138-c3a84bc4f689',\n",
              " 'e2cc2544-6147-4f1a-97b9-b8a0aa44426b',\n",
              " '9b0f41da-1b16-486e-be08-686f8975ef64',\n",
              " '6a2476dd-be70-4405-b764-efaefd0c6715',\n",
              " '6d680fda-37cd-4ab4-8cf0-3c22a41fd492',\n",
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              " '25bf014d-6e54-4db2-a298-504ed536ba63',\n",
              " 'd656d1eb-f0f2-44eb-a223-9aef8b6a3ec9',\n",
              " 'a5818b51-c655-4405-9ff5-9250126f2002',\n",
              " 'ad6c12be-df53-4d1b-9afb-b6b2d2c17451',\n",
              " '67576102-1b11-4cec-a82b-0565843c899d',\n",
              " '1fd22660-2b1f-4fa5-bc4b-999d46813a04',\n",
              " '11d8c13c-150a-4910-851d-4a84f9b13747',\n",
              " '62b42a67-d92b-438d-b128-8b754087eb4e',\n",
              " '4c930e3a-45ae-468e-8bb1-7c57c89abc4b',\n",
              " 'f2d87cde-6182-48b8-93ec-05ac72a05d09',\n",
              " '7203e561-b884-497a-b116-e605e585e167',\n",
              " '1e1c1bb0-17bf-437d-8826-e1c9a0120835',\n",
              " '6d9fe4e3-d2a8-438c-b179-9625e50a7ece',\n",
              " 'df9d8d58-5072-438d-b776-08f20076981c',\n",
              " 'bf0379d5-eb20-490a-8ea5-aec173c7f686',\n",
              " 'e50e90f5-9916-4793-b48e-2df3f7140535',\n",
              " '0de84b19-94a3-4843-a3af-a512d44e2d7d',\n",
              " 'dc4afe15-3f46-4f8b-b466-a45dffc54846',\n",
              " '2245486b-0834-4815-b393-9fb1ae71f675',\n",
              " '937ab5cb-33ba-4a90-9257-bac968951c86',\n",
              " '270ae3ec-c2f5-4222-a22c-25b7686ff6ba',\n",
              " '2bfc6f75-4f91-414b-8347-f990d96a6e91',\n",
              " 'ad947bd2-2c78-42e8-b02d-e2b806b80426',\n",
              " 'd2195687-a0e9-4338-82b4-f5be16967267',\n",
              " 'd45e0dd4-49ca-4968-82bc-a1fb6b6a0117',\n",
              " '13d95c05-111a-4eec-b5c9-46885c110700',\n",
              " '2a33eab8-9318-47be-9a35-a637ecbb1004',\n",
              " '7cd696fc-a5cf-47e1-910c-cd287ccd25a2',\n",
              " 'fc2aeb15-7a28-4b95-be9b-6f6d5ae9665e',\n",
              " '889443d0-1d8e-4624-8c7f-5eb66ed2f3d8',\n",
              " '933c9b99-181e-4ebb-a0f0-1593b1875fa9',\n",
              " '8826eeef-e0ef-45e9-ac9e-07a59657dbbd']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectorstore.as_retriever()"
      ],
      "metadata": {
        "id": "bFeI4n45_fxM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"Answer the question based only on the following context:\n",
        "{context}\n",
        "Question: {question}\n",
        "\"\"\"\n",
        "\n",
        "chain = (\n",
        "    RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
        "    | ChatPromptTemplate.from_template(template)\n",
        "    | ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\")\n",
        "    | StrOutputParser()\n",
        ")"
      ],
      "metadata": {
        "id": "8RgJSst8_3qU"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke(\"What is optimisitic rollup?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "5FI8gWrfAEVb",
        "outputId": "392c222a-1dd7-44e5-d31b-13c97288795a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Optimistic Rollup is a scalability solution for decentralized blockchains that locks the most value. It is based on the Ethereum Virtual Machine (EVM) and allows for strong development due to compatibility with existing Ethereum tools and the use of Solidity. It is a type of Rollup that accepts the output of blocks optimistically without verifying their execution.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain.invoke(\"What is the conclusion?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "cZ5eQj6fIqoz",
        "outputId": "0203bedd-235d-4a46-fdbc-e42f60a9cee9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The conclusion is that rollups are the most promising solution available today to solve the scalability problem in decentralized blockchains, ushering in the era of modular blockchains as opposed to monolithic blockchains.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    }
  ]
}