solutions/question-answering.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "tZnIXBfrRpex"
      },
      "source": [
        "# Question Answering with Langchain and OpenAI\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/generative-ai/question-answering.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "This interactive notebook uses Langchain to split fictional workplace documents into passages and uses OpenAI to transform these passages into embeddings and store them into Elasticsearch.\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Then when we ask a question, we retrieve the relevant passages from the vector store and use langchain and OpenAI to provide a summary for the question."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "GyAst2W-VpHb"
      },
      "source": [
        "## Import modules\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "33A-cP-XvFCr"
      },
      "outputs": [],
      "source": [
        "# import modules\n",
        "from getpass import getpass\n",
        "from langchain_openai import OpenAI\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "from langchain_elasticsearch import ElasticsearchStore\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.chains import RetrievalQA"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "qtEOCsCLWCZp"
      },
      "source": [
        "## Connect to Elasticsearch\n",
        "\n",
        "ℹ️ We're using an Elastic Cloud deployment of Elasticsearch for this notebook. If you don't have an Elastic Cloud deployment, sign up [here](https://cloud.elastic.co/registration?utm_source=github&utm_content=elasticsearch-labs-notebook) for a free trial. \n",
        "\n",
        "We'll use the **Cloud ID** to identify our deployment, because we are using Elastic Cloud deployment. To find the Cloud ID for your deployment, go to https://cloud.elastic.co/deployments and select your deployment.\n",
        "\n",
        "\n",
        "We will use [ElasticsearchStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.elasticsearch.ElasticsearchStore.html) to connect to our elastic cloud deployment. This would help create and index data easily. In the ElasticsearchStore instance, will set embedding to [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openai.OpenAIEmbeddings.html) to embed the texts and elasticsearch index name that will be used in this example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# set elastic cloud id and password\n",
        "CLOUD_ID = getpass(\"Elastic deployment Cloud ID\")\n",
        "CLOUD_USERNAME = getpass(\"Elastic username: \")\n",
        "CLOUD_PASSWORD = getpass(\"Elastic deployment Password\")\n",
        "\n",
        "# set OpenAI API key\n",
        "OPENAI_API_KEY = getpass(\"OpenAI API key\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "a-t1mglib54F"
      },
      "outputs": [],
      "source": [
        "embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)\n",
        "\n",
        "vector_store = ElasticsearchStore(es_cloud_id=CLOUD_ID, es_user=CLOUD_USERNAME, es_password=CLOUD_PASSWORD,\n",
        "            index_name= \"workplace_index\",\n",
        "            embedding=embeddings\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download the dataset \n",
        "\n",
        "Let's download the sample dataset and deserialize the document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "J8-93TiJsNyK"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully loaded 15 documents\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "\n",
        "path = \"./data/workplace-docs.json\"\n",
        "\n",
        "with open(path, \"r\") as f:\n",
        "    workplace_docs = json.loads(f.read())\n",
        "\n",
        "print(f\"Successfully loaded {len(workplace_docs)} documents\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "p0cQFDl1b9v4"
      },
      "source": [
        "### Split Documents into Passages\n",
        "\n",
        "We’ll chunk documents into passages in order to improve the retrieval specificity and to ensure that we can provide multiple passages within the context window of the final question answering prompt.\n",
        "\n",
        "Here we are chunking documents into 800 token passages with an overlap of 400 tokens.\n",
        "\n",
        "Here we are using a simple splitter but Langchain offers more advanced splitters to reduce the chance of context being lost."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "dbHEoTF6vBXE"
      },
      "outputs": [],
      "source": [
        "metadata = []\n",
        "content = []\n",
        "\n",
        "for doc in workplace_docs:\n",
        "  content.append(doc[\"content\"])\n",
        "  metadata.append({\n",
        "      \"name\": doc[\"name\"],\n",
        "      \"summary\": doc[\"summary\"]\n",
        "  })\n",
        "\n",
        "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=800, chunk_overlap=400)\n",
        "docs = text_splitter.create_documents(content, metadatas=metadata)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "RmCUl0hxW4lG"
      },
      "source": [
        "## Index data into elasticsearch\n",
        "\n",
        "Now that we have split each document into the chunk size of 800, we will now index data to elasticsearch using [ElasticsearchStore.from_documents](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html#langchain.vectorstores.elasticsearch.ElasticsearchStore.from_documents).\n",
        "\n",
        "We will use Cloud ID,  Password and Index name values set in the `Create cloud deployment` step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "documents = vector_store.from_documents(\n",
        "    docs, embeddings, es_cloud_id=CLOUD_ID, es_user=CLOUD_USERNAME, es_password=CLOUD_PASSWORD, index_name=\"workplace_index\"\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "rXJH_MiWejv7"
      },
      "source": [
        "## Asking a question\n",
        "Now that we have the passages stored in Elasticsearch, we can now ask a question to get the relevant passages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OobeBT6rek7Q",
        "outputId": "ba7b3a7a-253e-4e7f-83b9-cec07ebdac09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "---- Answer ----\n",
            " The NASA sales team is the North America South America region within the Americas region, led by two Area Vice-Presidents, Laura Martinez and Gary Johnson.\n",
            "---- Sources ----\n",
            "Sales Organization Overview\n",
            "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n",
            "\n",
            "The Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n",
            "\n",
            "Europe: Our European sales team covers the entire continent, including the United Kingdom, Germany, France, Spain, Italy, and other countries. The team is responsible for understanding the unique market dynamics and cultural nuances, enabling them to effectively target and engage with customers across the region. The Area Vice-President for Europe is Rajesh Patel.\n",
            "Asia-Pacific: This region encompasses countries such as China, Japan, South Korea, India, Australia, and New Zealand. Our sales team in the Asia-Pacific region works diligently to capitalize on growth opportunities and address the diverse needs of customers in this vast and rapidly evolving market. The Area Vice-President for Asia-Pacific is Mei Li.\n",
            "Middle East & Africa: This region comprises countries across the Middle East and Africa, such as the United Arab Emirates, Saudi Arabia, South Africa, and Nigeria. Our sales team in this region is responsible for navigating the unique market challenges and identifying opportunities to expand our presence and better serve our customers. The Area Vice-President for Middle East & Africa is Jamal Abdi.\n",
            "\n",
            "Each regional sales team consists of dedicated account managers, sales representatives, and support staff, led by their respective Area Vice-Presidents. They are responsible for identifying and pursuing new business opportunities, nurturing existing client relationships, and ensuring customer satisfaction. The teams collaborate closely with other departments, such as marketing, product development, and customer support, to ensure we consistently deliver high-quality products and services to our clients.\n",
            "Sales Organization Overview\n",
            "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n",
            "\n",
            "The Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n",
            "\n",
            "Europe: Our European sales team covers the entire continent, including the United Kingdom, Germany, France, Spain, Italy, and other countries. The team is responsible for understanding the unique market dynamics and cultural nuances, enabling them to effectively target and engage with customers across the region. The Area Vice-President for Europe is Rajesh Patel.\n",
            "Asia-Pacific: This region encompasses countries such as China, Japan, South Korea, India, Australia, and New Zealand. Our sales team in the Asia-Pacific region works diligently to capitalize on growth opportunities and address the diverse needs of customers in this vast and rapidly evolving market. The Area Vice-President for Asia-Pacific is Mei Li.\n",
            "Middle East & Africa: This region comprises countries across the Middle East and Africa, such as the United Arab Emirates, Saudi Arabia, South Africa, and Nigeria. Our sales team in this region is responsible for navigating the unique market challenges and identifying opportunities to expand our presence and better serve our customers. The Area Vice-President for Middle East & Africa is Jamal Abdi.\n",
            "\n",
            "Each regional sales team consists of dedicated account managers, sales representatives, and support staff, led by their respective Area Vice-Presidents. They are responsible for identifying and pursuing new business opportunities, nurturing existing client relationships, and ensuring customer satisfaction. The teams collaborate closely with other departments, such as marketing, product development, and customer support, to ensure we consistently deliver high-quality products and services to our clients.\n",
            "Sales Organization Overview\n",
            "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n",
            "\n",
            "The Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n",
            "\n",
            "Europe: Our European sales team covers the entire continent, including the United Kingdom, Germany, France, Spain, Italy, and other countries. The team is responsible for understanding the unique market dynamics and cultural nuances, enabling them to effectively target and engage with customers across the region. The Area Vice-President for Europe is Rajesh Patel.\n",
            "Asia-Pacific: This region encompasses countries such as China, Japan, South Korea, India, Australia, and New Zealand. Our sales team in the Asia-Pacific region works diligently to capitalize on growth opportunities and address the diverse needs of customers in this vast and rapidly evolving market. The Area Vice-President for Asia-Pacific is Mei Li.\n",
            "Middle East & Africa: This region comprises countries across the Middle East and Africa, such as the United Arab Emirates, Saudi Arabia, South Africa, and Nigeria. Our sales team in this region is responsible for navigating the unique market challenges and identifying opportunities to expand our presence and better serve our customers. The Area Vice-President for Middle East & Africa is Jamal Abdi.\n",
            "\n",
            "Each regional sales team consists of dedicated account managers, sales representatives, and support staff, led by their respective Area Vice-Presidents. They are responsible for identifying and pursuing new business opportunities, nurturing existing client relationships, and ensuring customer satisfaction. The teams collaborate closely with other departments, such as marketing, product development, and customer support, to ensure we consistently deliver high-quality products and services to our clients.\n",
            "Sales Organization Overview\n",
            "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n",
            "\n",
            "The Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n"
          ]
        }
      ],
      "source": [
        "retriever = vector_store.as_retriever()\n",
        "\n",
        "llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n",
        "\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=retriever,\n",
        "    return_source_documents=True\n",
        ")\n",
        "\n",
        "ans = qa.invoke({\"query\": \"what is the nasa sales team?\"})\n",
        "\n",
        "print(\"---- Answer ----\")\n",
        "print(ans[\"result\"])\n",
        "print(\"---- Sources ----\")\n",
        "for doc in ans[\"source_documents\"]:\n",
        "  print(doc.metadata[\"name\"])\n",
        "  print(doc.page_content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
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